Clearing the Dead: Apoptotic Cell Sensing, Recognition, Engulfment, and Digestion

PubMed Central

Hochreiter-Hufford, Amelia; Ravichandran, Kodi S.

2013-01-01

Clearance of apoptotic cells is the final stage of programmed cell death. Uncleared corpses can become secondarily necrotic, promoting inflammation and autoimmunity. Remarkably, even in tissues with high cellular turnover, apoptotic cells are rarely seen because of efficient clearance mechanisms in healthy individuals. Recently, significant progress has been made in understanding the steps involved in prompt cell clearance in vivo. These include the sensing of corpses via “find me” signals, the recognition of corpses via “eat me” signals and their cognate receptors, the signaling pathways that regulate cytoskeletal rearrangement necessary for engulfment, and the responses of the phagocyte that keep cell clearance events “immunologically silent.” This study focuses on our understanding of these steps. PMID:23284042

Escherichia coli twin arginine (Tat) mutant translocases possessing relaxed signal peptide recognition specificities.

PubMed

Kreutzenbeck, Peter; Kröger, Carsten; Lausberg, Frank; Blaudeck, Natascha; Sprenger, Georg A; Freudl, Roland

2007-03-16

The twin arginine (Tat) secretion pathway allows the translocation of folded proteins across the cytoplasmic membrane of bacteria. Tat-specific signal peptides contain a characteristic amino acid motif ((S/T)RRXFLK) including two highly conserved consecutive arginine residues that are thought to be involved in the recognition of the signal peptides by the Tat translocase. Here, we have analyzed the specificity of Tat signal peptide recognition by using a genetic approach. Replacement of the two arginine residues in a Tat-specific precursor protein by lysine-glutamine resulted in an export-defective mutant precursor that was no longer accepted by the wild-type translocase. Selection for restored export allowed for the isolation of Tat translocases possessing single mutations in either the amino-terminal domain of TatB or the first cytosolic domain of TatC. The mutant Tat translocases still efficiently accepted the unaltered precursor protein, indicating that the substrate specificity of the translocases was not strictly changed; rather, the translocases showed an increased tolerance toward variations of the amino acids occupying the positions of the twin arginine residues in the consensus motif of a Tat signal peptide.

Improvement of emotional healthcare system with stress detection from ECG signal.

PubMed

Tivatansakul, S; Ohkura, M

2015-01-01

Our emotional healthcare system is designed to cope with users' negative emotions in daily life. To make the system more intelligent, we integrated emotion recognition by facial expression to provide appropriate services based on user's current emotional state. Our emotion recognition by facial expression has confusion issue to recognize some positive, neutral and negative emotions that make the emotional healthcare system provide a relaxation service even though users don't have negative emotions. Therefore, to increase the effectiveness of the system to provide the relaxation service, we integrate stress detection from ECG signal. The stress detection might be able to address the confusion issue of emotion recognition by facial expression to provide the service. Indeed, our results show that integration of stress detection increases the effectiveness and efficiency of the emotional healthcare system to provide services.

The signal extraction of fetal heart rate based on wavelet transform and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

2005-04-01

This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.

Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise

PubMed Central

Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

2016-01-01

Vocabulary size has been suggested as a useful measure of “verbal abilities” that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18–35 years) and 22 older (60–78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults’ poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an average vocabulary size similar to that of younger adults, they were still slower in lexical access. PMID:27458400

Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise.

PubMed

Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

2016-01-01

Vocabulary size has been suggested as a useful measure of "verbal abilities" that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18-35 years) and 22 older (60-78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults' poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an average vocabulary size similar to that of younger adults, they were still slower in lexical access.

A split recognition mode combined with cascade signal amplification strategy for highly specific, sensitive detection of microRNA.

PubMed

Wang, Rui; Wang, Lei; Zhao, Haiyan; Jiang, Wei

2016-12-15

MicroRNAs (miRNAs) are vital for many biological processes and have been regarded as cancer biomarkers. Specific and sensitive detection of miRNAs is essential for cancer diagnosis and therapy. Herein, a split recognition mode combined with cascade signal amplification strategy is developed for highly specific and sensitive detection of miRNA. The split recognition mode possesses two specific recognition processes, which are based on toehold-mediated strand displacement reaction (TSDR) and direct hybridization reaction. Two recognition probes, hairpin probe (HP) with overhanging toehold domain and assistant probe (AP), are specially designed. Firstly, the toehold domain of HP and AP recognize part of miRNA simultaneously, accompanied with TSDR to unfold the HP and form the stable DNA Y-shaped junction structure (YJS). Then, the AP in YJS can further act as primer to initiate strand displacement amplification, releasing numerous trigger sequences. Finally, the trigger sequences hybridize with padlock DNA to initiate circular rolling circle amplification and generate enhanced fluorescence responses. In this strategy, the dual recognition effect of split recognition mode guarantees the excellent selectivity to discriminate let-7b from high-homology sequences. Furthermore, the high amplification efficiency of cascade signal amplification guarantees a high sensitivity with the detection limit of 3.2 pM and the concentration of let-7b in total RNA sample extracted from Hela cells is determined. These results indicate our strategy will be a promising miRNA detection strategy in clinical diagnosis and disease treatment. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative SERS Detection of Dopamine in Cerebrospinal Fluid by Dual-Recognition-Induced Hot Spot Generation.

PubMed

Zhang, Kun; Liu, Yu; Wang, Yuning; Zhang, Ren; Liu, Jiangang; Wei, Jia; Qian, Hufei; Qian, Kun; Chen, Ruoping; Liu, Baohong

2018-05-09

Reliable profiling of the extracellular dopamine (DA) concentration in the central nervous system is essential for a deep understanding of its biological and pathological functions. However, quantitative determination of this neurotransmitter remains a challenge because of the extremely low concentration of DA in the cerebrospinal fluid (CSF) of patients. Herein, on the basis of the specific recognition of boronate toward diol and N-hydroxysuccinimide ester toward the amine group, a simple and highly sensitive strategy was presented for DA detection by using surface-enhanced Raman scattering (SERS) spectroscopy as a signal readout. This was realized by first immobilizing 3,3'-dithiodipropionic acid di( N-hydroxysuccinimide ester) on gold thin film surfaces to capture DA, followed by introducing 3-mercaptophenylboronic acid (3-MPBA)-functionalized silver nanoparticles to generate numerous plasmonic "hot spots" with the nanoparticle-on-mirror geometry. Such a dual-recognition mechanism not only avoids complicated bioelement-based manipulations but also efficiently decreases the background signal. With the direct use of the recognition probe 3-MPBA as a Raman reporter, the "signal-on" SERS method was employed to quantify the concentration of DA from 1 pM to 1 μM with a detection limit of 0.3 pM. Moreover, our dual-recognition-directed SERS assay exhibited a high resistance to cerebral interference and was successfully applied to monitoring of DA in CSF samples of patients.

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation.

PubMed

Guan, Fengqing; Sun, Yu; Qi, Xiaozhi; Hu, Ying; Yu, Gang; Zhang, Jianwei

2018-05-09

Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.

Electrostatically Accelerated Encounter and Folding for Facile Recognition of Intrinsically Disordered Proteins

PubMed Central

Ganguly, Debabani; Zhang, Weihong; Chen, Jianhan

2013-01-01

Achieving facile specific recognition is essential for intrinsically disordered proteins (IDPs) that are involved in cellular signaling and regulation. Consideration of the physical time scales of protein folding and diffusion-limited protein-protein encounter has suggested that the frequent requirement of protein folding for specific IDP recognition could lead to kinetic bottlenecks. How IDPs overcome such potential kinetic bottlenecks to viably function in signaling and regulation in general is poorly understood. Our recent computational and experimental study of cell-cycle regulator p27 (Ganguly et al., J. Mol. Biol. (2012)) demonstrated that long-range electrostatic forces exerted on enriched charges of IDPs could accelerate protein-protein encounter via “electrostatic steering” and at the same time promote “folding-competent” encounter topologies to enhance the efficiency of IDP folding upon encounter. Here, we further investigated the coupled binding and folding mechanisms and the roles of electrostatic forces in the formation of three IDP complexes with more complex folded topologies. The surface electrostatic potentials of these complexes lack prominent features like those observed for the p27/Cdk2/cyclin A complex to directly suggest the ability of electrostatic forces to facilitate folding upon encounter. Nonetheless, similar electrostatically accelerated encounter and folding mechanisms were consistently predicted for all three complexes using topology-based coarse-grained simulations. Together with our previous analysis of charge distributions in known IDP complexes, our results support a prevalent role of electrostatic interactions in promoting efficient coupled binding and folding for facile specific recognition. These results also suggest that there is likely a co-evolution of IDP folded topology, charge characteristics, and coupled binding and folding mechanisms, driven at least partially by the need to achieve fast association kinetics for cellular signaling and regulation. PMID:24278008

A Robust and Device-Free System for the Recognition and Classification of Elderly Activities.

PubMed

Li, Fangmin; Al-Qaness, Mohammed Abdulaziz Aide; Zhang, Yong; Zhao, Bihai; Luan, Xidao

2016-12-01

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise.

The chemical structure of DNA sequence signals for RNA transcription

NASA Technical Reports Server (NTRS)

George, D. G.; Dayhoff, M. O.

1982-01-01

The proposed recognition sites for RNA transcription for E. coli NRA polymerase, bacteriophage T7 RNA polymerase, and eukaryotic RNA polymerase Pol II are evaluated in the light of the requirements for efficient recognition. It is shown that although there is good experimental evidence that specific nucleic acid sequence patterns are involved in transcriptional regulation in bacteria and bacterial viruses, among the sequences now available, only in the case of the promoters recognized by bacteriophage T7 polymerase does it seem likely that the pattern is sufficient. It is concluded that the eukaryotic pattern that is investigated is not restrictive enough to serve as a recognition site.

Automatic welding detection by an intelligent tool pipe inspection

NASA Astrophysics Data System (ADS)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Instrument-independent analysis of music by means of the continuous wavelet transform

NASA Astrophysics Data System (ADS)

Olmo, Gabriella; Dovis, Fabio; Benotto, Paolo; Calosso, Claudio; Passaro, Pierluigi

1999-10-01

This paper deals with the problem of automatic recognition of music. Segments of digitized music are processed by means of a Continuous Wavelet Transform, properly chosen so as to match the spectral characteristics of the signal. In order to achieve a good time-scale representation of the signal components a novel wavelet has been designed suited to the musical signal features. particular care has been devoted towards an efficient implementation, which operates in the frequency domain, and includes proper segmentation and aliasing reduction techniques to make the analysis of long signals feasible. The method achieves very good performance in terms of both time and frequency selectivity, and can yield the estimate and the localization in time of both the fundamental frequency and the main harmonics of each tone. The analysis is used as a preprocessing step for a recognition algorithm, which we show to be almost independent on the instrument reproducing the sounds. Simulations are provided to demonstrate the effectiveness of the proposed method.

Off-lexicon online Arabic handwriting recognition using neural network

NASA Astrophysics Data System (ADS)

Yahia, Hamdi; Chaabouni, Aymen; Boubaker, Houcine; Alimi, Adel M.

2017-03-01

This paper highlights a new method for online Arabic handwriting recognition based on graphemes segmentation. The main contribution of our work is to explore the utility of Beta-elliptic model in segmentation and features extraction for online handwriting recognition. Indeed, our method consists in decomposing the input signal into continuous part called graphemes based on Beta-Elliptical model, and classify them according to their position in the pseudo-word. The segmented graphemes are then described by the combination of geometric features and trajectory shape modeling. The efficiency of the considered features has been evaluated using feed forward neural network classifier. Experimental results using the benchmarking ADAB Database show the performance of the proposed method.

Optical signal processing using photonic reservoir computing

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Dehyadegari, Louiza

2014-10-01

As a new approach to recognition and classification problems, photonic reservoir computing has such advantages as parallel information processing, power efficient and high speed. In this paper, a photonic structure has been proposed for reservoir computing which is investigated using a simple, yet, non-partial noisy time series prediction task. This study includes the application of a suitable topology with self-feedbacks in a network of SOA's - which lends the system a strong memory - and leads to adjusting adequate parameters resulting in perfect recognition accuracy (100%) for noise-free time series, which shows a 3% improvement over previous results. For the classification of noisy time series, the rate of accuracy showed a 4% increase and amounted to 96%. Furthermore, an analytical approach was suggested to solve rate equations which led to a substantial decrease in the simulation time, which is an important parameter in classification of large signals such as speech recognition, and better results came up compared with previous works.

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

PubMed

Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

2018-04-01

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

Statistical process control using optimized neural networks: a case study.

PubMed

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

An effective approach for iris recognition using phase-based image matching.

PubMed

Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

2008-10-01

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

The chloroplast signal recognition particle (CpSRP) pathway as a tool to minimize chlorophyll antenna size and maximize photosynthetic productivity.

PubMed

Kirst, Henning; Melis, Anastasios

2014-01-01

The concept of the Truncated Light-harvesting chlorophyll Antenna (TLA) size, as a tool by which to maximize sunlight utilization and photosynthetic productivity in microalgal mass cultures or high-density plant canopies, is discussed. TLA technology is known to improve sunlight-to-product energy conversion efficiencies and is hereby exemplified by photosynthetic productivity estimates of wild type and a TLA strain under simulated mass culture conditions. Recent advances in the generation of TLA-type mutants by targeting genes of the chloroplast signal-recognition particle (CpSRP) pathway, affecting the thylakoid membrane assembly of light-harvesting proteins, are also summarized. Two distinct CpSRP assembly pathways are recognized, one entailing post-translational, the other a co-translational mechanism. Differences between the post-translational and co-translational integration mechanisms are outlined, as these pertain to the CpSRP-mediated assembly of thylakoid membrane protein complexes in higher plants and green microalgae. The applicability of the CpSRP pathway genes in efforts to generate TLA-type strains with enhanced solar energy conversion efficiency in photosynthesis is evaluated. © 2013.

Neural dynamics of audiovisual speech integration under variable listening conditions: an individual participant analysis

PubMed Central

Altieri, Nicholas; Wenger, Michael J.

2013-01-01

Speech perception engages both auditory and visual modalities. Limitations of traditional accuracy-only approaches in the investigation of audiovisual speech perception have motivated the use of new methodologies. In an audiovisual speech identification task, we utilized capacity (Townsend and Nozawa, 1995), a dynamic measure of efficiency, to quantify audiovisual integration. Capacity was used to compare RT distributions from audiovisual trials to RT distributions from auditory-only and visual-only trials across three listening conditions: clear auditory signal, S/N ratio of −12 dB, and S/N ratio of −18 dB. The purpose was to obtain EEG recordings in conjunction with capacity to investigate how a late ERP co-varies with integration efficiency. Results showed efficient audiovisual integration for low auditory S/N ratios, but inefficient audiovisual integration when the auditory signal was clear. The ERP analyses showed evidence for greater audiovisual amplitude compared to the unisensory signals for lower auditory S/N ratios (higher capacity/efficiency) compared to the high S/N ratio (low capacity/inefficient integration). The data are consistent with an interactive framework of integration, where auditory recognition is influenced by speech-reading as a function of signal clarity. PMID:24058358

Neural dynamics of audiovisual speech integration under variable listening conditions: an individual participant analysis.

PubMed

Altieri, Nicholas; Wenger, Michael J

2013-01-01

Speech perception engages both auditory and visual modalities. Limitations of traditional accuracy-only approaches in the investigation of audiovisual speech perception have motivated the use of new methodologies. In an audiovisual speech identification task, we utilized capacity (Townsend and Nozawa, 1995), a dynamic measure of efficiency, to quantify audiovisual integration. Capacity was used to compare RT distributions from audiovisual trials to RT distributions from auditory-only and visual-only trials across three listening conditions: clear auditory signal, S/N ratio of -12 dB, and S/N ratio of -18 dB. The purpose was to obtain EEG recordings in conjunction with capacity to investigate how a late ERP co-varies with integration efficiency. Results showed efficient audiovisual integration for low auditory S/N ratios, but inefficient audiovisual integration when the auditory signal was clear. The ERP analyses showed evidence for greater audiovisual amplitude compared to the unisensory signals for lower auditory S/N ratios (higher capacity/efficiency) compared to the high S/N ratio (low capacity/inefficient integration). The data are consistent with an interactive framework of integration, where auditory recognition is influenced by speech-reading as a function of signal clarity.

Access to Ribosomal Protein Rpl25p by the Signal Recognition Particle Is Required for Efficient Cotranslational Translocation

PubMed Central

Dalley, Jane A.; Selkirk, Alexander

2008-01-01

Targeting of proteins to the endoplasmic reticulum (ER) occurs cotranslationally necessitating the interaction of the signal recognition particle (SRP) and the translocon with the ribosome. Biochemical and structural studies implicate ribosomal protein Rpl25p as a major ribosome interaction site for both these factors. Here we characterize an RPL25GFP fusion, which behaves as a dominant mutant leading to defects in co- but not posttranslational translocation in vivo. In these cells, ribosomes still interact with ER membrane and the translocon, but are defective in binding SRP. Overexpression of SRP can restore ribosome binding of SRP, but only partially rescues growth and translocation defects. Our results indicate that Rpl25p plays a critical role in the recruitment of SRP to the ribosome. PMID:18448667

Conformational trapping of mismatch recognition complex MSH2/MSH3 on repair-resistant DNA loops.

PubMed

Lang, Walter H; Coats, Julie E; Majka, Jerzy; Hura, Greg L; Lin, Yuyen; Rasnik, Ivan; McMurray, Cynthia T

2011-10-18

Insertion and deletion of small heteroduplex loops are common mutations in DNA, but why some loops are prone to mutation and others are efficiently repaired is unknown. Here we report that the mismatch recognition complex, MSH2/MSH3, discriminates between a repair-competent and a repair-resistant loop by sensing the conformational dynamics of their junctions. MSH2/MSH3 binds, bends, and dissociates from repair-competent loops to signal downstream repair. Repair-resistant Cytosine-Adenine-Guanine (CAG) loops adopt a unique DNA junction that traps nucleotide-bound MSH2/MSH3, and inhibits its dissociation from the DNA. We envision that junction dynamics is an active participant and a conformational regulator of repair signaling, and governs whether a loop is removed by MSH2/MSH3 or escapes to become a precursor for mutation.

Structural Pattern Recognition Techniques for Data Retrieval in Massive Fusion Databases

NASA Astrophysics Data System (ADS)

Vega, J.; Murari, A.; Rattá, G. A.; Castro, P.; Pereira, A.; Portas, A.

2008-03-01

Diagnostics of present day reactor class fusion experiments, like the Joint European Torus (JET), generate thousands of signals (time series and video images) in each discharge. There is a direct correspondence between the physical phenomena taking place in the plasma and the set of structural shapes (patterns) that they form in the signals: bumps, unexpected amplitude changes, abrupt peaks, periodic components, high intensity zones or specific edge contours. A major difficulty related to data analysis is the identification, in a rapid and automated way, of a set of discharges with comparable behavior, i.e. discharges with "similar" patterns. Pattern recognition techniques are efficient tools to search for similar structural forms within the database in a fast an intelligent way. To this end, classification systems must be developed to be used as indexation methods to directly fetch the more similar patterns.

Intrinsic disorder in scaffold proteins: Getting more from less

PubMed Central

Cortese, Marc S.; Uversky, Vladimir N.; Dunker, A. Keith

2008-01-01

Regulation, recognition and cell signaling involve the coordinated actions of many players. Signaling scaffolds, with their ability to bring together proteins belonging to common and/or interlinked pathways, play crucial roles in orchestrating numerous events by coordinating specific interactions among signaling proteins. This review examines the roles of intrinsic disorder (ID) in signaling scaffold protein function. Several well-characterized scaffold proteins with structurally and functionally characterized ID regions are used here to illustrate the importance of ID for scaffolding function. These examples include scaffolds that are mostly disordered, only partially disordered or those in which the ID resides in a scaffold partner. Specific scaffolds discussed include RNase, voltage-activated potassium channels, axin, BRCA1, GSK-3β, p53, Ste5, titin, Fus3, BRCA1, Titin, MAP2, D-AKAP2 and AKAP250. Among the mechanisms discussed are: molecular recognition features, fly-casting, ease of encounter complex formation, structural isolation of partners, modulation of interactions between bound partners, masking of intramolecular interaction sites, maximized interaction surface per residue, toleration of high evolutionary rates, binding site overlap, allosteric modification, palindromic binding, reduced constraints for alternative splicing, efficient regulation via posttranslational modification, efficient regulation via rapid degradation, protection of normally solvent-exposed sites, enhancing the plasticity of interaction and molecular crowding. We conclude that ID can enhance scaffold function by a diverse array of mechanisms. In other words, scaffold proteins utilize several ID-facilitated mechanisms to enhance function, and by doing so, get more functionality from less structure. PMID:18619997

An improved PSO-SVM model for online recognition defects in eddy current testing

NASA Astrophysics Data System (ADS)

Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

2013-12-01

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Complex Event Recognition Architecture

NASA Technical Reports Server (NTRS)

Fitzgerald, William A.; Firby, R. James

2009-01-01

Complex Event Recognition Architecture (CERA) is the name of a computational architecture, and software that implements the architecture, for recognizing complex event patterns that may be spread across multiple streams of input data. One of the main components of CERA is an intuitive event pattern language that simplifies what would otherwise be the complex, difficult tasks of creating logical descriptions of combinations of temporal events and defining rules for combining information from different sources over time. In this language, recognition patterns are defined in simple, declarative statements that combine point events from given input streams with those from other streams, using conjunction, disjunction, and negation. Patterns can be built on one another recursively to describe very rich, temporally extended combinations of events. Thereafter, a run-time matching algorithm in CERA efficiently matches these patterns against input data and signals when patterns are recognized. CERA can be used to monitor complex systems and to signal operators or initiate corrective actions when anomalous conditions are recognized. CERA can be run as a stand-alone monitoring system, or it can be integrated into a larger system to automatically trigger responses to changing environments or problematic situations.

Adaptive method of recognition of signals for one and two-frequency signal system in the telephony on the background of speech

NASA Astrophysics Data System (ADS)

Kuznetsov, Michael V.

2006-05-01

For reliable teamwork of various systems of automatic telecommunication including transferring systems of optical communication networks it is necessary authentic recognition of signals for one- or two-frequency service signal system. The analysis of time parameters of an accepted signal allows increasing reliability of detection and recognition of the service signal system on a background of speech.

Substrate degradation by the proteasome: a single-molecule kinetic analysis

PubMed Central

Lu, Ying; Lee, Byung-hoon; King, Randall W; Finley, Daniel; Kirschner, Marc W

2015-01-01

To address how the configuration of conjugated ubiquitins determines the recognition of substrates by the proteasome, we analyzed the degradation kinetics of substrates with chemically defined ubiquitin configurations. Contrary to the view that a tetraubiquitin chain is the minimal signal for efficient degradation, we find that distributing the ubiquitins as diubiquitin chains provides a more efficient signal. To understand how the proteasome actually discriminates among ubiquitin configurations, we developed single-molecule assays that distinguished intermediate steps of degradation kinetically. The level of ubiquitin on a substrate drives proteasome-substrate interaction, whereas the chain structure of ubiquitin affects translocation into the axial channel on the proteasome. Together these two features largely determine the susceptibility of substrates for proteasomal degradation. PMID:25859050

Contribution of auditory working memory to speech understanding in mandarin-speaking cochlear implant users.

PubMed

Tao, Duoduo; Deng, Rui; Jiang, Ye; Galvin, John J; Fu, Qian-Jie; Chen, Bing

2014-01-01

To investigate how auditory working memory relates to speech perception performance by Mandarin-speaking cochlear implant (CI) users. Auditory working memory and speech perception was measured in Mandarin-speaking CI and normal-hearing (NH) participants. Working memory capacity was measured using forward digit span and backward digit span; working memory efficiency was measured using articulation rate. Speech perception was assessed with: (a) word-in-sentence recognition in quiet, (b) word-in-sentence recognition in speech-shaped steady noise at +5 dB signal-to-noise ratio, (c) Chinese disyllable recognition in quiet, (d) Chinese lexical tone recognition in quiet. Self-reported school rank was also collected regarding performance in schoolwork. There was large inter-subject variability in auditory working memory and speech performance for CI participants. Working memory and speech performance were significantly poorer for CI than for NH participants. All three working memory measures were strongly correlated with each other for both CI and NH participants. Partial correlation analyses were performed on the CI data while controlling for demographic variables. Working memory efficiency was significantly correlated only with sentence recognition in quiet when working memory capacity was partialled out. Working memory capacity was correlated with disyllable recognition and school rank when efficiency was partialled out. There was no correlation between working memory and lexical tone recognition in the present CI participants. Mandarin-speaking CI users experience significant deficits in auditory working memory and speech performance compared with NH listeners. The present data suggest that auditory working memory may contribute to CI users' difficulties in speech understanding. The present pattern of results with Mandarin-speaking CI users is consistent with previous auditory working memory studies with English-speaking CI users, suggesting that the lexical importance of voice pitch cues (albeit poorly coded by the CI) did not influence the relationship between working memory and speech perception.

Application of robust face recognition in video surveillance systems

NASA Astrophysics Data System (ADS)

Zhang, De-xin; An, Peng; Zhang, Hao-xiang

2018-03-01

In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

Hidden Markov models in automatic speech recognition

NASA Astrophysics Data System (ADS)

Wrzoskowicz, Adam

1993-11-01

This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

Acoustic interference and recognition space within a complex assemblage of dendrobatid frogs

PubMed Central

Amézquita, Adolfo; Flechas, Sandra Victoria; Lima, Albertina Pimentel; Gasser, Herbert; Hödl, Walter

2011-01-01

In species-rich assemblages of acoustically communicating animals, heterospecific sounds may constrain not only the evolution of signal traits but also the much less-studied signal-processing mechanisms that define the recognition space of a signal. To test the hypothesis that the recognition space is optimally designed, i.e., that it is narrower toward the species that represent the higher potential for acoustic interference, we studied an acoustic assemblage of 10 diurnally active frog species. We characterized their calls, estimated pairwise correlations in calling activity, and, to model the recognition spaces of five species, conducted playback experiments with 577 synthetic signals on 531 males. Acoustic co-occurrence was not related to multivariate distance in call parameters, suggesting a minor role for spectral or temporal segregation among species uttering similar calls. In most cases, the recognition space overlapped but was greater than the signal space, indicating that signal-processing traits do not act as strictly matched filters against sounds other than homospecific calls. Indeed, the range of the recognition space was strongly predicted by the acoustic distance to neighboring species in the signal space. Thus, our data provide compelling evidence of a role of heterospecific calls in evolutionarily shaping the frogs' recognition space within a complex acoustic assemblage without obvious concomitant effects on the signal. PMID:21969562

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

PubMed Central

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

Improved Open-Microphone Speech Recognition

NASA Astrophysics Data System (ADS)

Abrash, Victor

2002-12-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken dialog manager extra flexibility to recognize the signal with no audio gaps between recognition requests, as well as to rerecognize portions of the signal, or to rerecognize speech with different grammars, acoustic models, recognizers, start times, and so on. SRI expects that this new open-mic functionality will enable NASA to develop better error-correction mechanisms for spoken dialog systems, and may also enable new interaction strategies.

Improved Open-Microphone Speech Recognition

NASA Technical Reports Server (NTRS)

Abrash, Victor

2002-01-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken dialog manager extra flexibility to recognize the signal with no audio gaps between recognition requests, as well as to rerecognize portions of the signal, or to rerecognize speech with different grammars, acoustic models, recognizers, start times, and so on. SRI expects that this new open-mic functionality will enable NASA to develop better error-correction mechanisms for spoken dialog systems, and may also enable new interaction strategies.

Digital signal processing algorithms for automatic voice recognition

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1987-01-01

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

Threat as a feature in visual semantic object memory.

PubMed

Calley, Clifford S; Motes, Michael A; Chiang, H-Sheng; Buhl, Virginia; Spence, Jeffrey S; Abdi, Hervé; Anand, Raksha; Maguire, Mandy; Estevez, Leonardo; Briggs, Richard; Freeman, Thomas; Kraut, Michael A; Hart, John

2013-08-01

Threatening stimuli have been found to modulate visual processes related to perception and attention. The present functional magnetic resonance imaging (fMRI) study investigated whether threat modulates visual object recognition of man-made and naturally occurring categories of stimuli. Compared with nonthreatening pictures, threatening pictures of real items elicited larger fMRI BOLD signal changes in medial visual cortices extending inferiorly into the temporo-occipital (TO) "what" pathways. This region elicited greater signal changes for threatening items compared to nonthreatening from both the natural-occurring and man-made stimulus supraordinate categories, demonstrating a featural component to these visual processing areas. Two additional loci of signal changes within more lateral inferior TO areas (bilateral BA18 and 19 as well as the right ventral temporal lobe) were detected for a category-feature interaction, with stronger responses to man-made (category) threatening (feature) stimuli than to natural threats. The findings are discussed in terms of visual recognition of processing efficiently or rapidly groups of items that confer an advantage for survival. Copyright © 2012 Wiley Periodicals, Inc.

Binary zone-plate array for a parallel joint transform correlator applied to face recognition.

PubMed

Kodate, K; Hashimoto, A; Thapliya, R

1999-05-10

Taking advantage of small aberrations, high efficiency, and compactness, we developed a new, to our knowledge, design procedure for a binary zone-plate array (BZPA) and applied it to a parallel joint transform correlator for the recognition of the human face. Pairs of reference and unknown images of faces are displayed on a liquid-crystal spatial light modulator (SLM), Fourier transformed by the BZPA, intensity recorded on an optically addressable SLM, and inversely Fourier transformed to obtain correlation signals. Consideration of the bandwidth allows the relations among the channel number, the numerical aperture of the zone plates, and the pattern size to be determined. Experimentally a five-channel parallel correlator was implemented and tested successfully with a 100-person database. The design and the fabrication of a 20-channel BZPA for phonetic character recognition are also included.

An evolution based biosensor receptor DNA sequence generation algorithm.

PubMed

Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M; Lee, Jaewan; Zang, Yupeng

2010-01-01

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.

Social Communication and Vocal Recognition in Free-Ranging Rhesus Monkeys

NASA Astrophysics Data System (ADS)

Rendall, Christopher Andrew

Kinship and individual identity are key determinants of primate sociality, and the capacity for vocal recognition of individuals and kin is hypothesized to be an important adaptation facilitating intra-group social communication. Research was conducted on adult female rhesus monkeys on Cayo Santiago, Puerto Rico to test this hypothesis for three acoustically distinct calls characterized by varying selective pressures on communicating identity: coos (contact calls), grunts (close range social calls), and noisy screams (agonistic recruitment calls). Vocalization playback experiments confirmed a capacity for both individual and kin recognition of coos, but not screams (grunts were not tested). Acoustic analyses, using traditional spectrographic methods as well as linear predictive coding techniques, indicated that coos (but not grunts or screams) were highly distinctive, and that the effects of vocal tract filtering--formants --contributed more to statistical discriminations of both individuals and kin groups than did temporal or laryngeal source features. Formants were identified from very short (23 ms.) segments of coos and were stable within calls, indicating that formant cues to individual and kin identity were available throughout a call. This aspect of formant cues is predicted to be an especially important design feature for signaling identity efficiently in complex acoustic environments. Results of playback experiments involving manipulated coo stimuli provided preliminary perceptual support for the statistical inference that formant cues take precedence in facilitating vocal recognition. The similarity of formants among female kin suggested a mechanism for the development of matrilineal vocal signatures from the genetic and environmental determinants of vocal tract morphology shared among relatives. The fact that screams --calls strongly expected to communicate identity--were not individually distinctive nor recognized suggested the possibility that their acoustic structure and role in signaling identity might be constrained by functional or morphological design requirements associated with their role in signaling submission.

ESARR: enhanced situational awareness via road sign recognition

NASA Astrophysics Data System (ADS)

Perlin, V. E.; Johnson, D. B.; Rohde, M. M.; Lupa, R. M.; Fiorani, G.; Mohammad, S.

2010-04-01

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

NASA Astrophysics Data System (ADS)

Ma, Xiurong; Liu, Dan; Shan, Yunlong

2017-12-01

Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

Method and apparatus for obtaining complete speech signals for speech recognition applications

NASA Technical Reports Server (NTRS)

Abrash, Victor (Inventor); Cesari, Federico (Inventor); Franco, Horacio (Inventor); George, Christopher (Inventor); Zheng, Jing (Inventor)

2009-01-01

The present invention relates to a method and apparatus for obtaining complete speech signals for speech recognition applications. In one embodiment, the method continuously records an audio stream comprising a sequence of frames to a circular buffer. When a user command to commence or terminate speech recognition is received, the method obtains a number of frames of the audio stream occurring before or after the user command in order to identify an augmented audio signal for speech recognition processing. In further embodiments, the method analyzes the augmented audio signal in order to locate starting and ending speech endpoints that bound at least a portion of speech to be processed for recognition. At least one of the speech endpoints is located using a Hidden Markov Model.

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices.

PubMed

Liu, Wei; Kulin, Merima; Kazaz, Tarik; Shahid, Adnan; Moerman, Ingrid; De Poorter, Eli

2017-09-12

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems.

PubMed

Gao, Lei; Bourke, A K; Nelson, John

2014-06-01

Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with "light-weight" signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high recognition accuracies on a multi-sensor system. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures

PubMed Central

Pi, Yiming

2017-01-01

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar. PMID:29267249

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures.

PubMed

Zhou, Zhi; Cao, Zongjie; Pi, Yiming

2017-12-21

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

[Recognition of walking stance phase and swing phase based on moving window].

PubMed

Geng, Xiaobo; Yang, Peng; Wang, Xinran; Geng, Yanli; Han, Yu

2014-04-01

Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

On the recognition of emotional vocal expressions: motivations for a holistic approach.

PubMed

Esposito, Anna; Esposito, Antonietta M

2012-10-01

Human beings seem to be able to recognize emotions from speech very well and information communication technology aims to implement machines and agents that can do the same. However, to be able to automatically recognize affective states from speech signals, it is necessary to solve two main technological problems. The former concerns the identification of effective and efficient processing algorithms capable of capturing emotional acoustic features from speech sentences. The latter focuses on finding computational models able to classify, with an approximation as good as human listeners, a given set of emotional states. This paper will survey these topics and provide some insights for a holistic approach to the automatic analysis, recognition and synthesis of affective states.

Computational Burden Resulting from Image Recognition of High Resolution Radar Sensors

PubMed Central

López-Rodríguez, Patricia; Fernández-Recio, Raúl; Bravo, Ignacio; Gardel, Alfredo; Lázaro, José L.; Rufo, Elena

2013-01-01

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation. PMID:23609804

Computational burden resulting from image recognition of high resolution radar sensors.

PubMed

López-Rodríguez, Patricia; Fernández-Recio, Raúl; Bravo, Ignacio; Gardel, Alfredo; Lázaro, José L; Rufo, Elena

2013-04-22

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.

Multisensory emotion perception in congenitally, early, and late deaf CI users

PubMed Central

Nava, Elena; Villwock, Agnes K.; Büchner, Andreas; Lenarz, Thomas; Röder, Brigitte

2017-01-01

Emotions are commonly recognized by combining auditory and visual signals (i.e., vocal and facial expressions). Yet it is unknown whether the ability to link emotional signals across modalities depends on early experience with audio-visual stimuli. In the present study, we investigated the role of auditory experience at different stages of development for auditory, visual, and multisensory emotion recognition abilities in three groups of adolescent and adult cochlear implant (CI) users. CI users had a different deafness onset and were compared to three groups of age- and gender-matched hearing control participants. We hypothesized that congenitally deaf (CD) but not early deaf (ED) and late deaf (LD) CI users would show reduced multisensory interactions and a higher visual dominance in emotion perception than their hearing controls. The CD (n = 7), ED (deafness onset: <3 years of age; n = 7), and LD (deafness onset: >3 years; n = 13) CI users and the control participants performed an emotion recognition task with auditory, visual, and audio-visual emotionally congruent and incongruent nonsense speech stimuli. In different blocks, participants judged either the vocal (Voice task) or the facial expressions (Face task). In the Voice task, all three CI groups performed overall less efficiently than their respective controls and experienced higher interference from incongruent facial information. Furthermore, the ED CI users benefitted more than their controls from congruent faces and the CD CI users showed an analogous trend. In the Face task, recognition efficiency of the CI users and controls did not differ. Our results suggest that CI users acquire multisensory interactions to some degree, even after congenital deafness. When judging affective prosody they appear impaired and more strongly biased by concurrent facial information than typically hearing individuals. We speculate that limitations inherent to the CI contribute to these group differences. PMID:29023525

Multisensory emotion perception in congenitally, early, and late deaf CI users.

PubMed

Fengler, Ineke; Nava, Elena; Villwock, Agnes K; Büchner, Andreas; Lenarz, Thomas; Röder, Brigitte

2017-01-01

Emotions are commonly recognized by combining auditory and visual signals (i.e., vocal and facial expressions). Yet it is unknown whether the ability to link emotional signals across modalities depends on early experience with audio-visual stimuli. In the present study, we investigated the role of auditory experience at different stages of development for auditory, visual, and multisensory emotion recognition abilities in three groups of adolescent and adult cochlear implant (CI) users. CI users had a different deafness onset and were compared to three groups of age- and gender-matched hearing control participants. We hypothesized that congenitally deaf (CD) but not early deaf (ED) and late deaf (LD) CI users would show reduced multisensory interactions and a higher visual dominance in emotion perception than their hearing controls. The CD (n = 7), ED (deafness onset: <3 years of age; n = 7), and LD (deafness onset: >3 years; n = 13) CI users and the control participants performed an emotion recognition task with auditory, visual, and audio-visual emotionally congruent and incongruent nonsense speech stimuli. In different blocks, participants judged either the vocal (Voice task) or the facial expressions (Face task). In the Voice task, all three CI groups performed overall less efficiently than their respective controls and experienced higher interference from incongruent facial information. Furthermore, the ED CI users benefitted more than their controls from congruent faces and the CD CI users showed an analogous trend. In the Face task, recognition efficiency of the CI users and controls did not differ. Our results suggest that CI users acquire multisensory interactions to some degree, even after congenital deafness. When judging affective prosody they appear impaired and more strongly biased by concurrent facial information than typically hearing individuals. We speculate that limitations inherent to the CI contribute to these group differences.

Dual-process theory and signal-detection theory of recognition memory.

PubMed

Wixted, John T

2007-01-01

Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know procedure, and both methods are now widely used in the neuroscience literature to identify the brain correlates of recollection and familiarity. However, in recent years, a substantial literature has accumulated directly contrasting the signal-detection model against the threshold/detection model, and that literature is almost unanimous in its endorsement of signal-detection theory. A dual-process version of signal-detection theory implies that individual recognition decisions are not process pure, and it suggests new ways to investigate the brain correlates of recognition memory. ((c) 2007 APA, all rights reserved).

Individual recognition based on communication behaviour of male fowl.

PubMed

Smith, Carolynn L; Taubert, Jessica; Weldon, Kimberly; Evans, Christopher S

2016-04-01

Correctly directing social behaviour towards a specific individual requires an ability to discriminate between conspecifics. The mechanisms of individual recognition include phenotype matching and familiarity-based recognition. Communication-based recognition is a subset of familiarity-based recognition wherein the classification is based on behavioural or distinctive signalling properties. Male fowl (Gallus gallus) produce a visual display (tidbitting) upon finding food in the presence of a female. Females typically approach displaying males. However, males may tidbit without food. We used the distinctiveness of the visual display and the unreliability of some males to test for communication-based recognition in female fowl. We manipulated the prior experience of the hens with the males to create two classes of males: S(+) wherein the tidbitting signal was paired with a food reward to the female, and S (-) wherein the tidbitting signal occurred without food reward. We then conducted a sequential discrimination test with hens using a live video feed of a familiar male. The results of the discrimination tests revealed that hens discriminated between categories of males based on their signalling behaviour. These results suggest that fowl possess a communication-based recognition system. This is the first demonstration of live-to-video transfer of recognition in any species of bird. Copyright © 2016 Elsevier B.V. All rights reserved.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

PubMed

Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

2015-12-01

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

One process is not enough! A speed-accuracy tradeoff study of recognition memory.

PubMed

Boldini, Angela; Russo, Riccardo; Avons, S E

2004-04-01

Speed-accuracy tradeoff (SAT) methods have been used to contrast single- and dual-process accounts of recognition memory. In these procedures, subjects are presented with individual test items and are required to make recognition decisions under various time constraints. In this experiment, we presented word lists under incidental learning conditions, varying the modality of presentation and level of processing. At test, we manipulated the interval between each visually presented test item and a response signal, thus controlling the amount of time available to retrieve target information. Study-test modality match had a beneficial effect on recognition accuracy at short response-signal delays (< or =300 msec). Conversely, recognition accuracy benefited more from deep than from shallow processing at study only at relatively long response-signal delays (> or =300 msec). The results are congruent with views suggesting that both fast familiarity and slower recollection processes contribute to recognition memory.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

A novel feature ranking algorithm for biometric recognition with PPG signals.

PubMed

Reşit Kavsaoğlu, A; Polat, Kemal; Recep Bozkurt, M

2014-06-01

This study is intended for describing the application of the Photoplethysmography (PPG) signal and the time domain features acquired from its first and second derivatives for biometric identification. For this purpose, a sum of 40 features has been extracted and a feature-ranking algorithm is proposed. This proposed algorithm calculates the contribution of each feature to biometric recognition and collocates the features, the contribution of which is from great to small. While identifying the contribution of the features, the Euclidean distance and absolute distance formulas are used. The efficiency of the proposed algorithms is demonstrated by the results of the k-NN (k-nearest neighbor) classifier applications of the features. During application, each 15-period-PPG signal belonging to two different durations from each of the thirty healthy subjects were used with a PPG data acquisition card. The first PPG signals recorded from the subjects were evaluated as the 1st configuration; the PPG signals recorded later at a different time as the 2nd configuration and the combination of both were evaluated as the 3rd configuration. When the results were evaluated for the k-NN classifier model created along with the proposed algorithm, an identification of 90.44% for the 1st configuration, 94.44% for the 2nd configuration, and 87.22% for the 3rd configuration has successfully been attained. The obtained results showed that both the proposed algorithm and the biometric identification model based on this developed PPG signal are very promising for contactless recognizing the people with the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Tie2 and Eph Receptor Tyrosine Kinase Activation and Signaling

PubMed Central

Barton, William A.; Dalton, Annamarie C.; Seegar, Tom C.M.; Himanen, Juha P.

2014-01-01

The Eph and Tie cell surface receptors mediate a variety of signaling events during development and in the adult organism. As other receptor tyrosine kinases, they are activated on binding of extracellular ligands and their catalytic activity is tightly regulated on multiple levels. The Eph and Tie receptors display some unique characteristics, including the requirement of ligand-induced receptor clustering for efficient signaling. Interestingly, both Ephs and Ties can mediate different, even opposite, biological effects depending on the specific ligand eliciting the response and on the cellular context. Here we discuss the structural features of these receptors, their interactions with various ligands, as well as functional implications for downstream signaling initiation. The Eph/ephrin structures are already well reviewed and we only provide a brief overview on the initial binding events. We go into more detail discussing the Tie-angiopoietin structures and recognition. PMID:24478383

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

The Effect of Asymmetrical Signal Degradation on Binaural Speech Recognition in Children and Adults.

ERIC Educational Resources Information Center

Rothpletz, Ann M.; Tharpe, Anne Marie; Grantham, D. Wesley

2004-01-01

To determine the effect of asymmetrical signal degradation on binaural speech recognition, 28 children and 14 adults were administered a sentence recognition task amidst multitalker babble. There were 3 listening conditions: (a) monaural, with mild degradation in 1 ear; (b) binaural, with mild degradation in both ears (symmetric degradation); and…

The role of multimodal signals in species recognition between tree-killing bark beetles in a narrow sympatric zone.

Treesearch

Deepa S. Pureswaran; Richard W. Hofstetter; Brian Sullivan; Kristen A. Potter

2016-01-01

When related species coexist, selection pressure should favor evolution of species recognition mechanisms to prevent interspecific pairing and wasteful reproductive encounters. We investigated the potential role of pheromone and acoustic signals in species recognition between two species of tree-killing bark beetles, the southern pine beetle, Dendroctonus frontalis...

Optical information-processing systems and architectures II; Proceedings of the Meeting, San Diego, CA, July 9-13, 1990

NASA Astrophysics Data System (ADS)

Javidi, Bahram

The present conference discusses topics in the fields of neural networks, acoustooptic signal processing, pattern recognition, phase-only processing, nonlinear signal processing, image processing, optical computing, and optical information processing. Attention is given to the optical implementation of an inner-product neural associative memory, optoelectronic associative recall via motionless-head/parallel-readout optical disk, a compact real-time acoustooptic image correlator, a multidimensional synthetic estimation filter, and a light-efficient joint transform optical correlator. Also discussed are a high-resolution spatial light modulator, compact real-time interferometric Fourier-transform processors, a fast decorrelation algorithm for permutation arrays, the optical interconnection of optical modules, and carry-free optical binary adders.

Advances in audio source seperation and multisource audio content retrieval

NASA Astrophysics Data System (ADS)

Vincent, Emmanuel

2012-06-01

Audio source separation aims to extract the signals of individual sound sources from a given recording. In this paper, we review three recent advances which improve the robustness of source separation in real-world challenging scenarios and enable its use for multisource content retrieval tasks, such as automatic speech recognition (ASR) or acoustic event detection (AED) in noisy environments. We present a Flexible Audio Source Separation Toolkit (FASST) and discuss its advantages compared to earlier approaches such as independent component analysis (ICA) and sparse component analysis (SCA). We explain how cues as diverse as harmonicity, spectral envelope, temporal fine structure or spatial location can be jointly exploited by this toolkit. We subsequently present the uncertainty decoding (UD) framework for the integration of audio source separation and audio content retrieval. We show how the uncertainty about the separated source signals can be accurately estimated and propagated to the features. Finally, we explain how this uncertainty can be efficiently exploited by a classifier, both at the training and the decoding stage. We illustrate the resulting performance improvements in terms of speech separation quality and speaker recognition accuracy.

[The endpoint detection of cough signal in continuous speech].

PubMed

Yang, Guoqing; Mo, Hongqiang; Li, Wen; Lian, Lianfang; Zheng, Zeguang

2010-06-01

The endpoint detection of cough signal in continuous speech has been researched in order to improve the efficiency and veracity of manual recognition or computer-based automatic recognition. First, using the short time zero crossing ratio(ZCR) for identifying the suspicious coughs and getting the threshold of short time energy based on acoustic characteristics of cough. Then, the short time energy is combined with short time ZCR in order to implement the endpoint detection of cough in continuous speech. To evaluate the effect of the method, first, the virtual number of coughs in each recording was identified by two experienced doctors using the graphical user interface (GUI). Second, the recordings were analyzed by automatic endpoint detection program under Matlab7.0. Finally, the comparison between these two results showed: The error rate of undetected cough is 2.18%, and 98.13% of noise, silence and speech were removed. The way of setting short time energy threshold is robust. The endpoint detection program can remove most speech and noise, thus maintaining a lower rate of error.

Algorithm, applications and evaluation for protein comparison by Ramanujan Fourier transform.

PubMed

Zhao, Jian; Wang, Jiasong; Hua, Wei; Ouyang, Pingkai

2015-12-01

The amino acid sequence of a protein determines its chemical properties, chain conformation and biological functions. Protein sequence comparison is of great importance to identify similarities of protein structures and infer their functions. Many properties of a protein correspond to the low-frequency signals within the sequence. Low frequency modes in protein sequences are linked to the secondary structures, membrane protein types, and sub-cellular localizations of the proteins. In this paper, we present Ramanujan Fourier transform (RFT) with a fast algorithm to analyze the low-frequency signals of protein sequences. The RFT method is applied to similarity analysis of protein sequences with the Resonant Recognition Model (RRM). The results show that the proposed fast RFT method on protein comparison is more efficient than commonly used discrete Fourier transform (DFT). RFT can detect common frequencies as significant feature for specific protein families, and the RFT spectrum heat-map of protein sequences demonstrates the information conservation in the sequence comparison. The proposed method offers a new tool for pattern recognition, feature extraction and structural analysis on protein sequences. Copyright © 2015 Elsevier Ltd. All rights reserved.

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

PubMed

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition.

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition

PubMed Central

Song, QingJun; Jiang, HaiYan; Song, Qinghui; Zhao, XieGuang; Wu, Xiaoxuan

2017-01-01

Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant analysis framework for coal-rock recognition. In the framework, a data acquisition model with vibration and acoustic signals is designed and the caving dataset with 10 feature variables and three classes is got. And the perfect combination of feature variables can be automatically decided by using the multi-class F-score (MF-Score) feature selection. In terms of nonlinear mapping in real-world optimization problem, an effective minimum enclosing ball (MEB) algorithm plus Support vector machine (SVM) is proposed for rapid detection of coal-rock in the caving process. In particular, we illustrate how to construct MEB-SVM classifier in coal-rock recognition which exhibit inherently complex distribution data. The proposed method is examined on UCI data sets and the caving dataset, and compared with some new excellent SVM classifiers. We conduct experiments with accuracy and Friedman test for comparison of more classifiers over multiple on the UCI data sets. Experimental results demonstrate that the proposed algorithm has good robustness and generalization ability. The results of experiments on the caving dataset show the better performance which leads to a promising feature selection and multi-class recognition in coal-rock recognition. PMID:28937987

A mechatronics platform to study prosthetic hand control using EMG signals.

PubMed

Geethanjali, P

2016-09-01

In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time. This prototype facilitated the study of various issues of pattern recognition and identified an efficient classifier, along with a feature ensemble, in the implementation of EMG controlled prosthetic hands in a laboratory setting at low-cost. This platform may help to motivate and facilitate prosthetic hand research in developing countries.

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems

PubMed Central

Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. PMID:29425248

Natural user interface as a supplement of the holographic Raman tweezers

NASA Astrophysics Data System (ADS)

Tomori, Zoltan; Kanka, Jan; Kesa, Peter; Jakl, Petr; Sery, Mojmir; Bernatova, Silvie; Antalik, Marian; Zemánek, Pavel

2014-09-01

Holographic Raman tweezers (HRT) manipulates with microobjects by controlling the positions of multiple optical traps via the mouse or joystick. Several attempts have appeared recently to exploit touch tablets, 2D cameras or Kinect game console instead. We proposed a multimodal "Natural User Interface" (NUI) approach integrating hands tracking, gestures recognition, eye tracking and speech recognition. For this purpose we exploited "Leap Motion" and "MyGaze" low-cost sensors and a simple speech recognition program "Tazti". We developed own NUI software which processes signals from the sensors and sends the control commands to HRT which subsequently controls the positions of trapping beams, micropositioning stage and the acquisition system of Raman spectra. System allows various modes of operation proper for specific tasks. Virtual tools (called "pin" and "tweezers") serving for the manipulation with particles are displayed on the transparent "overlay" window above the live camera image. Eye tracker identifies the position of the observed particle and uses it for the autofocus. Laser trap manipulation navigated by the dominant hand can be combined with the gestures recognition of the secondary hand. Speech commands recognition is useful if both hands are busy. Proposed methods make manual control of HRT more efficient and they are also a good platform for its future semi-automated and fully automated work.

Complex network inference from P300 signals: Decoding brain state under visual stimulus for able-bodied and disabled subjects

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Cai, Qing; Dong, Na; Zhang, Shan-Shan; Bo, Yun; Zhang, Jie

2016-10-01

Distinguishing brain cognitive behavior underlying disabled and able-bodied subjects constitutes a challenging problem of significant importance. Complex network has established itself as a powerful tool for exploring functional brain networks, which sheds light on the inner workings of the human brain. Most existing works in constructing brain network focus on phase-synchronization measures between regional neural activities. In contrast, we propose a novel approach for inferring functional networks from P300 event-related potentials by integrating time and frequency domain information extracted from each channel signal, which we show to be efficient in subsequent pattern recognition. In particular, we construct brain network by regarding each channel signal as a node and determining the edges in terms of correlation of the extracted feature vectors. A six-choice P300 paradigm with six different images is used in testing our new approach, involving one able-bodied subject and three disabled subjects suffering from multiple sclerosis, cerebral palsy, traumatic brain and spinal-cord injury, respectively. We then exploit global efficiency, local efficiency and small-world indices from the derived brain networks to assess the network topological structure associated with different target images. The findings suggest that our method allows identifying brain cognitive behaviors related to visual stimulus between able-bodied and disabled subjects.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

PubMed Central

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

Does the Butcher-on-the-Bus Phenomenon Require a Dual-Process Explanation? A Signal Detection Analysis

PubMed Central

Tunney, Richard J.; Mullett, Timothy L.; Moross, Claudia J.; Gardner, Anna

2012-01-01

The butcher-on-the-bus is a rhetorical device or hypothetical phenomenon that is often used to illustrate how recognition decisions can be based on different memory processes (Mandler, 1980). The phenomenon describes a scenario in which a person is recognized but the recognition is accompanied by a sense of familiarity or knowing characterized by an absence of contextual details such as the person’s identity. We report two recognition memory experiments that use signal detection analyses to determine whether this phenomenon is evidence for a recollection plus familiarity model of recognition or is better explained by a univariate signal detection model. We conclude that there is an interaction between confidence estimates and remember-know judgments which is not explained fully by either single-process signal detection or traditional dual-process models. PMID:22745631

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

PubMed

Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

2016-02-25

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Robot Command Interface Using an Audio-Visual Speech Recognition System

NASA Astrophysics Data System (ADS)

Ceballos, Alexánder; Gómez, Juan; Prieto, Flavio; Redarce, Tanneguy

In recent years audio-visual speech recognition has emerged as an active field of research thanks to advances in pattern recognition, signal processing and machine vision. Its ultimate goal is to allow human-computer communication using voice, taking into account the visual information contained in the audio-visual speech signal. This document presents a command's automatic recognition system using audio-visual information. The system is expected to control the laparoscopic robot da Vinci. The audio signal is treated using the Mel Frequency Cepstral Coefficients parametrization method. Besides, features based on the points that define the mouth's outer contour according to the MPEG-4 standard are used in order to extract the visual speech information.

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

PubMed

Zhang, Yanjun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

2016-02-01

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

Models of Recognition, Repetition Priming, and Fluency : Exploring a New Framework

ERIC Educational Resources Information Center

Berry, Christopher J.; Shanks, David R.; Speekenbrink, Maarten; Henson, Richard N. A.

2012-01-01

We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely…

Noise tolerance in optical waveguide circuits for recognition of optical 16 quadrature amplitude modulation codes

NASA Astrophysics Data System (ADS)

Inoshita, Kensuke; Hama, Yoshimitsu; Kishikawa, Hiroki; Goto, Nobuo

2016-12-01

In photonic label routers, various optical signal processing functions are required; these include optical label extraction, recognition of the label, optical switching and buffering controlled by signals based on the label information and network routing tables, and label rewriting. Among these functions, we focus on photonic label recognition. We have proposed two kinds of optical waveguide circuits to recognize 16 quadrature amplitude modulation codes, i.e., recognition from the minimum output port and from the maximum output port. The recognition function was theoretically analyzed and numerically simulated by finite-difference beam-propagation method. We discuss noise tolerance in the circuit and show numerically simulated results to evaluate bit-error-rate (BER) characteristics against optical signal-to-noise ratio (OSNR). The OSNR required to obtain a BER less than 1.0×10-3 for the symbol rate of 2.5 GBaud was 14.5 and 27.0 dB for recognition from the minimum and maximum output, respectively.

Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

NASA Astrophysics Data System (ADS)

Cyganek, Boguslaw; Smolka, Bogdan

2015-02-01

In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen

NASA Technical Reports Server (NTRS)

Hinton, Yolanda L.

1999-01-01

Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

RIG-I in RNA virus recognition

PubMed Central

Kell, Alison M.; Gale, Michael

2015-01-01

Antiviral immunity is initiated upon host recognition of viral products via non-self molecular patterns known as pathogen-associated molecular patterns (PAMPs). Such recognition initiates signaling cascades that induce intracellular innate immune defenses and an inflammatory response that facilitates development of the acquired immune response. The retinoic acid-inducible gene I (RIG-I) and the RIG-I-like receptor (RLR) protein family are key cytoplasmic pathogen recognition receptors that are implicated in the recognition of viruses across genera and virus families, including functioning as major sensors of RNA viruses, and promoting recognition of some DNA viruses. RIG-I, the charter member of the RLR family, is activated upon binding to PAMP RNA. Activated RIG-I signals by interacting with the adapter protein MAVS leading to a signaling cascade that activates the transcription factors IRF3 and NF-κB. These actions induce the expression of antiviral gene products and the production of type I and III interferons that lead to an antiviral state in the infected cell and surrounding tissue. RIG-I signaling is essential for the control of infection by many RNA viruses. Recently, RIG-I crosstalk with other pathogen recognition receptors and components of the inflammasome has been described. In this review, we discuss the current knowledge regarding the role of RIG-I in recognition of a variety of virus families and its role in programming the adaptive immune response through cross-talk with parallel arms of the innate immune system, including how RIG-I can be leveraged for antiviral therapy. PMID:25749629

Recognition Memory zROC Slopes for Items with Correct versus Incorrect Source Decisions Discriminate the Dual Process and Unequal Variance Signal Detection Models

ERIC Educational Resources Information Center

Starns, Jeffrey J.; Rotello, Caren M.; Hautus, Michael J.

2014-01-01

We tested the dual process and unequal variance signal detection models by jointly modeling recognition and source confidence ratings. The 2 approaches make unique predictions for the slope of the recognition memory zROC function for items with correct versus incorrect source decisions. The standard bivariate Gaussian version of the unequal…

Processing Electromyographic Signals to Recognize Words

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Lee, D. D.

2009-01-01

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

Testing Signal-Detection Models of Yes/No and Two-Alternative Forced-Choice Recognition Memory

ERIC Educational Resources Information Center

Jang, Yoonhee; Wixted, John T.; Huber, David E.

2009-01-01

The current study compared 3 models of recognition memory in their ability to generalize across yes/no and 2-alternative forced-choice (2AFC) testing. The unequal-variance signal-detection model assumes a continuous memory strength process. The dual-process signal-detection model adds a thresholdlike recollection process to a continuous…

EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

PubMed

Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

2016-01-20

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

Identification of exosite residues of factor Xa involved in recognition of PAR-2 on endothelial cells.

PubMed

Manithody, Chandrashekhara; Yang, Likui; Rezaie, Alireza R

2012-03-27

Recent results have indicated that factor Xa (FXa) cleaves protease-activated receptor 2 (PAR-2) to elicit protective intracellular signaling responses in endothelial cells. In this study, we investigated the molecular determinants of the specificity of FXa interaction with PAR-2 by monitoring the cleavage of PAR-2 by FXa in endothelial cells transiently transfected with a PAR-2 cleavage reporter construct in which the extracellular domain of the receptor was fused to cDNA encoding for alkaline phosphatase. Comparison of the cleavage efficiency of PAR-2 by a series of FXa mutants containing mutations in different surface loops indicated that the acidic residues of 39-loop (Glu-36, Glu-37, and Glu-39) and the basic residues of 60-loop (Lys-62 and Arg-63), 148-loop (Arg-143, Arg-150, and Arg-154), and 162-helix (Arg-165 and Lys-169) contribute to the specificity of receptor recognition by FXa on endothelial cells. This was evidenced by significantly reduced activity of mutants toward PAR-2 expressed on transfected cells. The extent of loss in the PAR-2 cleavage activity of FXa mutants correlated with the extent of loss in their PAR-2-dependent intracellular signaling activity. Further characterization of FXa mutants indicated that, with the exception of basic residues of 162-helix, which play a role in the recognition specificity of the prothrombinase complex, none of the surface loop residues under study makes a significant contribution to the activity of FXa in the prothrombinase complex. These results provide new insight into mechanisms through which FXa specifically interacts with its macromolecular substrates in the clotting and signaling pathways.

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores.

PubMed

El-Safty, Sherif A; Shenashen, Mohamed A; Sakai, Masaru; Elshehy, Emad; Halada, Kohmei

2015-12-06

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in industrialized countries. Here, the development of optical mesosensors/adsorbents (MSAs) for efficient recognition and selective recovery of Pd(II), Au(III), and Co(II) from urban mine was achieved. A simple, general method for preparing MSAs based on using high-order mesoporous monolithic scaffolds was described. Hierarchical cubic Ia3d wagon-wheel-shaped MSAs were fabricated by anchoring chelating agents (colorants) into three-dimensional pores and micrometric particle surfaces of the mesoporous monolithic scaffolds. Findings show, for the first time, evidence of controlled optical recognition of Pd(II), Au(III), and Co(II) ions and a highly selective system for recovery of Pd(II) ions (up to ~95%) in ores and industrial wastes. Furthermore, the controlled assessment processes described herein involve evaluation of intrinsic properties (e.g., visual signal change, long-term stability, adsorption efficiency, extraordinary sensitivity, selectivity, and reusability); thus, expensive, sophisticated instruments are not required. Results show evidence that MSAs will attract worldwide attention as a promising technological means of recovering and recycling palladium, gold and cobalt metals.

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

PubMed Central

El-Safty, Sherif A.; Shenashen, Mohamed A.; Sakai, Masaru; Elshehy, Emad; Halada, Kohmei

2015-01-01

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in industrialized countries. Here, the development of optical mesosensors/adsorbents (MSAs) for efficient recognition and selective recovery of Pd(II), Au(III), and Co(II) from urban mine was achieved. A simple, general method for preparing MSAs based on using high-order mesoporous monolithic scaffolds was described. Hierarchical cubic Ia3d wagon-wheel-shaped MSAs were fabricated by anchoring chelating agents (colorants) into three-dimensional pores and micrometric particle surfaces of the mesoporous monolithic scaffolds. Findings show, for the first time, evidence of controlled optical recognition of Pd(II), Au(III), and Co(II) ions and a highly selective system for recovery of Pd(II) ions (up to ~95%) in ores and industrial wastes. Furthermore, the controlled assessment processes described herein involve evaluation of intrinsic properties (e.g., visual signal change, long-term stability, adsorption efficiency, extraordinary sensitivity, selectivity, and reusability); thus, expensive, sophisticated instruments are not required. Results show evidence that MSAs will attract worldwide attention as a promising technological means of recovering and recycling palladium, gold and cobaltmetals. PMID:26709467

How landmark suitability shapes recognition memory signals for objects in the medial temporal lobes.

PubMed

Martin, Chris B; Sullivan, Jacqueline A; Wright, Jessey; Köhler, Stefan

2018-02-01

A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings > trees > aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability. Copyright © 2017 Elsevier Inc. All rights reserved.

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Neural Dynamics Underlying Target Detection in the Human Brain

PubMed Central

Bansal, Arjun K.; Madhavan, Radhika; Agam, Yigal; Golby, Alexandra; Madsen, Joseph R.

2014-01-01

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in an image, the brain compares input signals and goals to elicit the correct behavior. We examined how target detection modulates visual recognition signals by recording intracranial field potential responses from 776 electrodes in 10 epileptic human subjects. We observed reliable differences in the physiological responses to stimuli when a cued target was present versus absent. Goal-related modulation was particularly strong in the inferior temporal and fusiform gyri, two areas important for object recognition. Target modulation started after 250 ms post stimulus, considerably after the onset of visual recognition signals. While broadband signals exhibited increased or decreased power, gamma frequency power showed predominantly increases during target presence. These observations support models where task goals interact with sensory inputs via top-down signals that influence the highest echelons of visual processing after the onset of selective responses. PMID:24553944

Signal recognition and parameter estimation of BPSK-LFM combined modulation

NASA Astrophysics Data System (ADS)

Long, Chao; Zhang, Lin; Liu, Yu

2015-07-01

Intra-pulse analysis plays an important role in electronic warfare. Intra-pulse feature abstraction focuses on primary parameters such as instantaneous frequency, modulation, and symbol rate. In this paper, automatic modulation recognition and feature extraction for combined BPSK-LFM modulation signals based on decision theoretic approach is studied. The simulation results show good recognition effect and high estimation precision, and the system is easy to be realized.

The recognition signal hypothesis for the adaptive evolution of religion : a phylogenetic test with Christian denominations.

PubMed

Matthews, Luke J

2012-06-01

Recent research on the evolution of religion has focused on whether religion is an unselected by-product of evolutionary processes or if it is instead an adaptation by natural selection. Adaptive hypotheses for religion include direct fitness benefits from improved health and indirect fitness benefits mediated by costly signals and/or cultural group selection. Herein, I propose that religious denominations achieve indirect fitness gains for members through the use of ecologically arbitrary beliefs, rituals, and moral rules that function as recognition markers of cultural inheritance analogous to kin and species recognition of genetic inheritance in biology. This recognition signal hypotheses could act in concert with either costly signaling or cultural group selection to produce evolutionarily altruistic behaviors within denominations. Using a cultural phylogenetic analysis, I show that a large set of religious behaviors among extant Christian denominations supports the prediction of the recognition signal hypothesis that characters change more frequently near historical schisms. By incorporating demographic data into the model, I show that more-distinctive denominations, as measured through dissimilar characteristics, appear to be protected from intrusion by nonmembers in mixed-denomination households, and that they may be experiencing greater biological growth of their populations even in the present day.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Interspecific aggression and character displacement of competitor recognition in Hetaerina damselflies.

PubMed

Anderson, Christopher N; Grether, Gregory F

2010-02-22

In zones of sympatry between closely related species, species recognition errors in a competitive context can cause character displacement in agonistic signals and competitor recognition functions, just as species recognition errors in a mating context can cause character displacement in mating signals and mate recognition. These two processes are difficult to distinguish because the same traits can serve as both agonistic and mating signals. One solution is to test for sympatric shifts in recognition functions. We studied competitor recognition in Hetaerina damselflies by challenging territory holders with live tethered conspecific and heterospecific intruders. Heterospecific intruders elicited less aggression than conspecific intruders in species pairs with dissimilar wing coloration (H. occisa/H. titia, H. americana/H. titia) but not in species pairs with similar wing coloration (H. occisa/H. cruentata, H. americana/H. cruentata). Natural variation in the area of black wing pigmentation on H. titia intruders correlated negatively with heterospecific aggression. To directly examine the role of wing coloration, we blackened the wings of H. occisa or H. americana intruders and measured responses of conspecific territory holders. This treatment reduced territorial aggression at multiple sites where H. titia is present, but not at allopatric sites. These results provide strong evidence for agonistic character displacement.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

"Multiple partial recognitions in dynamic equilibrium" in the binding sites of proteins form the molecular basis of promiscuous recognition of structurally diverse ligands.

PubMed

Kohda, Daisuke

2018-04-01

Promiscuous recognition of ligands by proteins is as important as strict recognition in numerous biological processes. In living cells, many short, linear amino acid motifs function as targeting signals in proteins to specify the final destination of the protein transport. In general, the target signal is defined by a consensus sequence containing wild-characters, and hence represented by diverse amino acid sequences. The classical lock-and-key or induced-fit/conformational selection mechanism may not cover all aspects of the promiscuous recognition. On the basis of our crystallographic and NMR studies on the mitochondrial Tom20 protein-presequence interaction, we proposed a new hypothetical mechanism based on "a rapid equilibrium of multiple states with partial recognitions". This dynamic, multiple recognition mode enables the Tom20 receptor to recognize diverse mitochondrial presequences with nearly equal affinities. The plant Tom20 is evolutionally unrelated to the animal Tom20 in our study, but is a functional homolog of the animal/fungal Tom20. NMR studies by another research group revealed that the presequence binding by the plant Tom20 was not fully explained by simple interaction modes, suggesting the presence of a similar dynamic, multiple recognition mode. Circumstantial evidence also suggested that similar dynamic mechanisms may be applicable to other promiscuous recognitions of signal peptides by the SRP54/Ffh and SecA proteins.

Method of synthesized phase objects for pattern recognition with rotation invariance

NASA Astrophysics Data System (ADS)

Ostroukh, Alexander P.; Butok, Alexander M.; Shvets, Rostislav A.; Yezhov, Pavel V.; Kim, Jin-Tae; Kuzmenko, Alexander V.

2015-11-01

We present a development of the method of synthesized phase objects (SPO-method) [1] for the rotation-invariant pattern recognition. For the standard method of recognition and the SPO-method, the comparison of the parameters of correlation signals for a number of amplitude objects is executed at the realization of a rotation in an optical-digital correlator with the joint Fourier transformation. It is shown that not only the invariance relative to a rotation at a realization of the joint correlation for synthesized phase objects (SP-objects) but also the main advantage of the method of SP-objects over the reference one such as the unified δ-like recognition signal with the largest possible signal-to-noise ratio independent of the type of an object are attained.

[Perception of emotional intonation of noisy speech signal with different acoustic parameters by adults of different age and gender].

PubMed

Dmitrieva, E S; Gel'man, V Ia

2011-01-01

The listener-distinctive features of recognition of different emotional intonations (positive, negative and neutral) of male and female speakers in the presence or absence of background noise were studied in 49 adults aged 20-79 years. In all the listeners noise produced the most pronounced decrease in recognition accuracy for positive emotional intonation ("joy") as compared to other intonations, whereas it did not influence the recognition accuracy of "anger" in 65-79-year-old listeners. The higher emotion recognition rates of a noisy signal were observed for speech emotional intonations expressed by female speakers. Acoustic characteristics of noisy and clear speech signals underlying perception of speech emotional prosody were found for adult listeners of different age and gender.

Applications of Wavelet Transform and Fuzzy Neural Network on Power Quality Recognition

NASA Astrophysics Data System (ADS)

Liao, Chiung-Chou; Yang, Hong-Tzer; Lin, Ying-Chun

2008-10-01

The wavelet transform coefficients (WTCs) contain plenty of information needed for transient event identification of power quality (PQ) events. However, adopting WTCs directly has the drawbacks of taking a longer time and too much memory for the recognition system. To solve the abovementioned recognition problems and to effectively reduce the number of features representing power transients, spectrum energies of WTCs in different scales are calculated by Parseval's Theorem. Through the proposed approach, features of the original power signals can be reserved and not influenced by occurring points of PQ events. The fuzzy neural classification systems are then used for signal recognition and fuzzy rule construction. Success rates of recognizing PQ events from noise-riding signals are proven to be feasible in power system applications in this paper.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

Anxiety dissociates the adaptive functions of sensory and motor response enhancements to social threats

PubMed Central

El Zein, Marwa; Wyart, Valentin; Grèzes, Julie

2015-01-01

Efficient detection and reaction to negative signals in the environment is essential for survival. In social situations, these signals are often ambiguous and can imply different levels of threat for the observer, thereby making their recognition susceptible to contextual cues – such as gaze direction when judging facial displays of emotion. However, the mechanisms underlying such contextual effects remain poorly understood. By computational modeling of human behavior and electrical brain activity, we demonstrate that gaze direction enhances the perceptual sensitivity to threat-signaling emotions – anger paired with direct gaze, and fear paired with averted gaze. This effect arises simultaneously in ventral face-selective and dorsal motor cortices at 200 ms following face presentation, dissociates across individuals as a function of anxiety, and does not reflect increased attention to threat-signaling emotions. These findings reveal that threat tunes neural processing in fast, selective, yet attention-independent fashion in sensory and motor systems, for different adaptive purposes. DOI: http://dx.doi.org/10.7554/eLife.10274.001 PMID:26712157

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition.

PubMed

Lu, Zhiyuan; Chen, Xiang; Zhang, Xu; Tong, Kay-Yu; Zhou, Ping

2017-08-01

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Safe trajectory estimation at a pedestrian crossing to assist visually impaired people.

PubMed

Alghamdi, Saleh; van Schyndel, Ron; Khalil, Ibrahim

2012-01-01

The aim of this paper is to present a service for blind and people with low vision to assist them to cross the street independently. The presented approach provides the user with significant information such as detection of pedestrian crossing signal from any point of view, when the pedestrian crossing signal light is green, the detection of dynamic and fixed obstacles, predictions of the movement of fellow pedestrians and information on objects which may intersect his path. Our approach is based on capturing multiple frames using a depth camera which is attached to a user's headgear. Currently a testbed system is built on a helmet and is connected to a laptop in the user's backpack. In this paper, we discussed efficiency of using Speeded-Up Robust Features (SURF) algorithm for object recognition for purposes of blind people assistance. The system predicts the movement of objects of interest to provide the user with information on the safest path to navigate and information on the surrounding area. Evaluation of this approach on real sequence video frames provides 90% of human detection and more than 80% for recognition of other related objects.

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

PubMed

Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

2017-03-19

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

PubMed Central

Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

2017-01-01

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity. PMID:28335492

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

Novel DNA packaging recognition in the unusual bacteriophage N15

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feiss, Michael; Geyer, Henriette, E-mail: henriettegeyer@gmail.com; Division of Viral Infections, Robert Koch Institute, Berlin

Phage lambda's cosB packaging recognition site is tripartite, consisting of 3 TerS binding sites, called R sequences. TerS binding to the critical R3 site positions the TerL endonuclease for nicking cosN to generate cohesive ends. The N15 cos (cos{sup N15}) is closely related to cos{sup λ}, but whereas the cosB{sup N15} subsite has R3, it lacks the R2 and R1 sites and the IHF binding site of cosB{sup λ}. A bioinformatic study of N15-like phages indicates that cosB{sup N15} also has an accessory, remote rR2 site, which is proposed to increase packaging efficiency, like R2 and R1 of lambda. N15more » plus five prophages all have the rR2 sequence, which is located in the TerS-encoding 1 gene, approximately 200 bp distal to R3. An additional set of four highly related prophages, exemplified by Monarch, has R3 sequence, but also has R2 and R1 sequences characteristic of cosB–λ. The DNA binding domain of TerS-N15 is a dimer. - Highlights: • There are two classes of DNA packaging signals in N15-related phages. • Phage N15's TerS binding site: a critical site and a possible remote accessory site. • Viral DNA recognition signals by the λ-like bacteriophages: the odd case of N15.« less

Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices

PubMed Central

Liu, Wei; Kulin, Merima; Kazaz, Tarik; De Poorter, Eli

2017-01-01

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals’ modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI’s probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access. PMID:28895879

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 3 2012-01-01 2012-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 3 2014-01-01 2014-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 3 2011-01-01 2011-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 3 2013-01-01 2013-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 3 2010-01-01 2010-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

PubMed

Selvaraj, Lokesh; Ganesan, Balakrishnan

2014-01-01

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Waveguide-type optical circuits for recognition of optical 8QAM-coded label

NASA Astrophysics Data System (ADS)

Surenkhorol, Tumendemberel; Kishikawa, Hiroki; Goto, Nobuo; Gonchigsumlaa, Khishigjargal

2017-10-01

Optical signal processing is expected to be applied in network nodes. In photonic routers, label recognition is one of the important functions. We have studied different kinds of label recognition methods so far for on-off keying, binary phase-shift keying, quadrature phase-shift keying, and 16 quadrature amplitude modulation-coded labels. We propose a method based on waveguide circuits to recognize an optical eight quadrature amplitude modulation (8QAM)-coded label by simple passive optical signal processing. The recognition of the proposed method is theoretically analyzed and numerically simulated by the finite difference beam propagation method. The noise tolerance is discussed, and bit-error rate against optical signal-to-noise ratio is evaluated. The scalability of the proposed method is also discussed theoretically for two-symbol length 8QAM-coded labels.

Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system

NASA Astrophysics Data System (ADS)

Poinsot, Audrey; Yang, Fan; Brost, Vincent

2011-02-01

Including multiple sources of information in personal identity recognition and verification gives the opportunity to greatly improve performance. We propose a contactless biometric system that combines two modalities: palmprint and face. Hardware implementations are proposed on the Texas Instrument Digital Signal Processor and Xilinx Field-Programmable Gate Array (FPGA) platforms. The algorithmic chain consists of a preprocessing (which includes palm extraction from hand images), Gabor feature extraction, comparison by Hamming distance, and score fusion. Fusion possibilities are discussed and tested first using a bimodal database of 130 subjects that we designed (uB database), and then two common public biometric databases (AR for face and PolyU for palmprint). High performance has been obtained for recognition and verification purpose: a recognition rate of 97.49% with AR-PolyU database and an equal error rate of 1.10% on the uB database using only two training samples per subject have been obtained. Hardware results demonstrate that preprocessing can easily be performed during the acquisition phase, and multimodal biometric recognition can be treated almost instantly (0.4 ms on FPGA). We show the feasibility of a robust and efficient multimodal hardware biometric system that offers several advantages, such as user-friendliness and flexibility.

Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition

NASA Astrophysics Data System (ADS)

Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

2018-03-01

The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference array) and fragments with diferent dimensions for clustering is carried out. The experiments, using the software environment Mathcad showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. The experimental results show that such models can be successfully used for auto- and hetero-associative recognition. Also they can be used to explain some mechanisms, known as "the reinforcementinhibition concept". Also we demonstrate a real model experiments, which confirm that the nonlinear processing by equivalent function allow to determine the neuron-winners and customize the weight matrix. At the end of the report, we will show how to use the obtained results and to propose new more efficient hardware architecture of SL_EC_RMNS based on matrix-tensor multipliers. Also we estimate the parameters and performance of such architectures.

Deep learning with coherent nanophotonic circuits

NASA Astrophysics Data System (ADS)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Dynamic Spectral Structure Specifies Vowels for Adults and Children

PubMed Central

Nittrouer, Susan; Lowenstein, Joanna H.

2014-01-01

The dynamic specification account of vowel recognition suggests that formant movement between vowel targets and consonant margins is used by listeners to recognize vowels. This study tested that account by measuring contributions to vowel recognition of dynamic (i.e., time-varying) spectral structure and coarticulatory effects on stationary structure. Adults and children (four-and seven-year-olds) were tested with three kinds of consonant-vowel-consonant syllables: (1) unprocessed; (2) sine waves that preserved both stationary coarticulated and dynamic spectral structure; and (3) vocoded signals that primarily preserved that stationary, but not dynamic structure. Sections of two lengths were removed from syllable middles: (1) half the vocalic portion; and (2) all but the first and last three pitch periods. Adults performed accurately with unprocessed and sine-wave signals, as long as half the syllable remained; their recognition was poorer for vocoded signals, but above chance. Seven-year-olds performed more poorly than adults with both sorts of processed signals, but disproportionately worse with vocoded than sine-wave signals. Most four-year-olds were unable to recognize vowels at all with vocoded signals. Conclusions were that both dynamic and stationary coarticulated structures support vowel recognition for adults, but children attend to dynamic spectral structure more strongly because early phonological organization favors whole words. PMID:25536845

Developing Signal-Pattern-Recognition Programs

NASA Technical Reports Server (NTRS)

Shelton, Robert O.; Hammen, David

2006-01-01

Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

A Novel Energy-Efficient Approach for Human Activity Recognition

PubMed Central

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Tang, Biyu; Lu, Hai; Shi, Haibin

2017-01-01

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. PMID:28885560

Intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory.

PubMed

Takeda, Atsushi; Tamano, Haruna; Ogawa, Taisuke; Takada, Shunsuke; Nakamura, Masatoshi; Fujii, Hiroaki; Ando, Masaki

2014-11-01

The role of perforant pathway-dentate granule cell synapses in cognitive behavior was examined focusing on synaptic Zn(2+) signaling in the dentate gyrus. Object recognition memory was transiently impaired when extracellular Zn(2+) levels were decreased by injection of clioquinol and N,N,N',N'-tetrakis-(2-pyridylmethyl) ethylendediamine. To pursue the effect of the loss and/or blockade of Zn(2+) signaling in dentate granule cells, ZnAF-2DA (100 pmol, 0.1 mM/1 µl), an intracellular Zn(2+) chelator, was locally injected into the dentate molecular layer of rats. ZnAF-2DA injection, which was estimated to chelate intracellular Zn(2+) signaling only in the dentate gyrus, affected object recognition memory 1 h after training without affecting intracellular Ca(2+) signaling in the dentate molecular layer. In vivo dentate gyrus long-term potentiation (LTP) was affected under the local perfusion of the recording region (the dentate granule cell layer) with 0.1 mM ZnAF-2DA, but not with 1-10 mM CaEDTA, an extracellular Zn(2+) chelator, suggesting that the blockade of intracellular Zn(2+) signaling in dentate granule cells affects dentate gyrus LTP. The present study demonstrates that intracellular Zn(2+) signaling in the dentate gyrus is required for object recognition memory, probably via dentate gyrus LTP expression. Copyright © 2014 Wiley Periodicals, Inc.

Dual-Process Theory and Signal-Detection Theory of Recognition Memory

ERIC Educational Resources Information Center

Wixted, John T.

2007-01-01

Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know…

Non-parallel coevolution of sender and receiver in the acoustic communication system of treefrogs.

PubMed

Schul, Johannes; Bush, Sarah L

2002-09-07

Advertisement calls of closely related species often differ in quantitative features such as the repetition rate of signal units. These differences are important in species recognition. Current models of signal-receiver coevolution predict two possible patterns in the evolution of the mechanism used by receivers to recognize the call: (i) classical sexual selection models (Fisher process, good genes/indirect benefits, direct benefits models) predict that close relatives use qualitatively similar signal recognition mechanisms tuned to different values of a call parameter; and (ii) receiver bias models (hidden preference, pre-existing bias models) predict that if different signal recognition mechanisms are used by sibling species, evidence of an ancestral mechanism will persist in the derived species, and evidence of a pre-existing bias will be detectable in the ancestral species. We describe qualitatively different call recognition mechanisms in sibling species of treefrogs. Whereas Hyla chrysoscelis uses pulse rate to recognize male calls, Hyla versicolor uses absolute measurements of pulse duration and interval duration. We found no evidence of either hidden preferences or pre-existing biases. The results are compared with similar data from katydids (Tettigonia sp.). In both taxa, the data are not adequately explained by current models of signal-receiver coevolution.

Hetero-enzyme-based two-round signal amplification strategy for trace detection of aflatoxin B1 using an electrochemical aptasensor.

PubMed

Zheng, Wanli; Teng, Jun; Cheng, Lin; Ye, Yingwang; Pan, Daodong; Wu, Jingjing; Xue, Feng; Liu, Guodong; Chen, Wei

2016-06-15

An electrochemical aptasensor for trace detection of aflatoxin B1 (AFB1) was developed by using an aptamer as the recognition unit while adopting the telomerase and EXO III based two-round signal amplification strategy as the signal enhancement units. The telomerase amplification was used to elongate the ssDNA probes on the surface of gold nanoparticles, by which the signal response range of the signal-off model electrochemical aptasensor could be correspondingly enlarged. Then, the EXO III amplification was used to hydrolyze the 3'-end of the dsDNA after the recognition of target AFB1, which caused the release of bounded AFB1 into the sensing system, where it participated in the next recognition-sensing cycle. With this two-round signal amplified electrochemical aptasensor, target AFB1 was successfully measured at trace concentrations with excellent detection limit of 0.6*10(-4)ppt and satisfied specificity due to the excellent affinity of the aptamer against AFB1. Based on this designed two-round signal amplification strategy, both the sensing range and detection limit were greatly improved. This proposed ultrasensitive electrochemical aptasensor method was also validated by comparison with the classic instrumental methods. Importantly, this hetero-enzyme based two-round signal amplified electrochemical aptasensor offers a great promising protocol for ultrasensitive detection of AFB1 and other mycotoxins by replacing the core recognition sequence of the aptamer. Copyright © 2016 Elsevier B.V. All rights reserved.

Motion-based signaling in sympatric species of Australian agamid lizards.

PubMed

Ramos, Jose A; Peters, Richard A

2017-08-01

Signaling species occurring in sympatry are often exposed to similar environmental constraints, so similar adaptations to enhance signal efficacy are expected. However, potentially opposing selective pressures might be present to ensure species recognition. Here, we analyzed the movement-based signals of two pairs of sympatric lizard species to consider how reliable communication is maintained while avoiding misidentification. Our novel approach allows us to quantify signal contrast with plant motion noise at any site we measure, including those utilized by other species. Ctenophorus caudicinctus and Gowidon longirostris differed in display complexity and motor pattern use. They also differed in overall morphology, but their signal contrast scores are strikingly similar. These results demonstrate similar adaptations to their shared environment while maintaining species recognition cues. In contrast, Ctenophorus fordi and Ctenophorus pictus are much closer in appearance, but C. pictus produces considerably higher signal contrast scores, which we suggest is attributable to the absence of territoriality in C. fordi. Taken together, our data provide evidence for adaptation to the local environment in movement-based signals, while also meeting species recognition requirements, but the selective pressure to deal with local conditions is mediated by signal function.

[Surface electromyography signal classification using gray system theory].

PubMed

Xie, Hongbo; Ma, Congbin; Wang, Zhizhong; Huang, Hai

2004-12-01

A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.

Efficient and Tunable Three-Dimensional Functionalization of Fully Zwitterionic Antifouling Surface Coatings.

PubMed

Lange, Stefanie C; van Andel, Esther; Smulders, Maarten M J; Zuilhof, Han

2016-10-11

To enhance the sensitivity and selectivity of surface-based (bio)sensors, it is of crucial importance to diminish background signals that arise from the nonspecific binding of biomolecules, so-called biofouling. Zwitterionic polymer brushes have been shown to be excellent antifouling materials. However, for sensing purposes, antifouling does not suffice but needs to be combined with the possibility to efficiently modify the brush with recognition units. So far this has been achieved only at the expense of either antifouling properties or binding capacity. Herein we present a conceptually new approach by integrating both characteristics into a single tailor-made monomer: a novel sulfobetaine-based zwitterionic monomer equipped with a clickable azide moiety. Copolymerization of this monomer with a well-established standard sulfobetaine monomer results in highly antifouling surface coatings with a large yet tunable number of clickable groups present throughout the entire brush. Subsequent functionalization of the azido brushes via widely used strain-promoted alkyne azide click reactions yields fully zwitterionic 3D-functionalized coatings with a recognition unit of choice that can be tailored for any specific application. Here we show a proof of principle with biotin-functionalized brushes on Si 3 N 4 that combine excellent antifouling properties with specific avidin binding from a protein mixture. The signal-to-noise ratio is significantly improved over that of traditional chain-end modification of sulfobetaine polymer brushes, even if the azide content is lowered to 1%. This therefore offers a viable approach to the development of biosensors with greatly enhanced performance on any surface.

Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

NASA Astrophysics Data System (ADS)

Winda, A.; Sofyan; Sthevany; Vincent, R. S.

2017-12-01

Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

Detection of Terrorist Preparations by an Artificial Intelligence Expert System Employing Fuzzy Signal Detection Theory

DTIC Science & Technology

2004-10-25

FUSEDOT does not require facial recognition , or video surveillance of public areas, both of which are apparently a component of TIA ([26], pp...does not use fuzzy signal detection. Involves facial recognition and video surveillance of public areas. Involves monitoring the content of voice...fuzzy signal detection, which TIA does not. Second, FUSEDOT would be easier to develop, because it does not require the development of facial

Representation, Classification and Information Fusion for Robust and Efficient Multimodal Human States Recognition

ERIC Educational Resources Information Center

Li, Ming

2013-01-01

The goal of this work is to enhance the robustness and efficiency of the multimodal human states recognition task. Human states recognition can be considered as a joint term for identifying/verifing various kinds of human related states, such as biometric identity, language spoken, age, gender, emotion, intoxication level, physical activity, vocal…

Flexible Piezoelectric Sensor-Based Gait Recognition.

PubMed

Cha, Youngsu; Kim, Hojoon; Kim, Doik

2018-02-05

Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

Detection and recognition of targets by using signal polarization properties

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Peralta-Fabi, Ricardo; Popov, Anatoly V.; Babakov, Mikhail F.

1999-08-01

The quality of radar target recognition can be enhanced by exploiting its polarization signatures. A specialized X-band polarimetric radar was used for target recognition in experimental investigations. The following polarization characteristics connected to the object geometrical properties were investigated: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy of a backscattering signal; object shape factor. A large quantity of polarimetric radar data was measured and processed to form a database of different object and different weather conditions. The histograms of polarization signatures were approximated by a Nakagami distribution, then used for real- time target recognition. The Neyman-Pearson criterion was used for the target detection, and the criterion of the maximum of a posterior probability was used for recognition problem. Some results of experimental verification of pattern recognition and detection of objects with different electrophysical and geometrical characteristics urban in clutter are presented in this paper.

Bioinspired Pollen-Like Hierarchical Surface for Efficient Recognition of Target Cancer Cells.

PubMed

Wang, Wenshuo; Yang, Gao; Cui, Haijun; Meng, Jingxin; Wang, Shutao; Jiang, Lei

2017-08-01

The efficient recognition and isolation of rare cancer cells holds great promise for cancer diagnosis and prognosis. In nature, pollens exploit spiky structures to realize recognition and adhesion to stigma. Herein, a bioinspired pollen-like hierarchical surface is developed by replicating the assembly of pollen grains, and efficient and specific recognition to target cancer cells is achieved. The pollen-like surface is fabricated by combining filtering-assisted assembly and soft lithography-based replication of pollen grains of wild chrysanthemum. After modification with a capture agent specific to cancer cells, the pollen-like surface enables the capture of target cancer cells with high efficiency and specificity. In addition, the pollen-like surface not only assures high viability of captured cells but also performs well in cell mixture system and at low cell density. This study represents a good example of constructing cell recognition biointerfaces inspired by pollen-stigma adhesion. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

10 CFR 431.21 - Procedures for recognition and withdrawal of recognition of accreditation bodies and...

Code of Federal Regulations, 2011 CFR

2011-01-01

... ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR CERTAIN COMMERCIAL AND INDUSTRIAL EQUIPMENT Electric Motors Test Procedures, Materials Incorporated and Methods of Determining Efficiency § 431.21 Procedures... Assistant Secretary for Energy Efficiency and Renewable Energy, U.S. Department of Energy, Forrestal...

On the Measurement of Criterion Noise in Signal Detection Theory: The Case of Recognition Memory

ERIC Educational Resources Information Center

Kellen, David; Klauer, Karl Christoph; Singmann, Henrik

2012-01-01

Traditional approaches within the framework of signal detection theory (SDT; Green & Swets, 1966), especially in the field of recognition memory, assume that the positioning of response criteria is not a noisy process. Recent work (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008) has challenged this assumption, arguing not only…

Investigating Strength and Frequency Effects in Recognition Memory Using Type-2 Signal Detection Theory

ERIC Educational Resources Information Center

Higham, Philip A.; Perfect, Timothy J.; Bruno, Davide

2009-01-01

Criterion- versus distribution-shift accounts of frequency and strength effects in recognition memory were investigated with Type-2 signal detection receiver operating characteristic (ROC) analysis, which provides a measure of metacognitive monitoring. Experiment 1 demonstrated a frequency-based mirror effect, with a higher hit rate and lower…

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

Host-microbe interactions in the small bowel

PubMed Central

Davies, Julie M.; Abreu, Maria T.

2015-01-01

Purpose of Review The intestine, home to a vast microbiome, balances its immune reactivity on a knife’s edge. This review will summarize recent studies examining innate immune signals that shape the microbiota, and how pathogens can usurp protective responses to their advantage. Recent findings Innate signaling uses several pathways to maintain epithelial defense. Toll-like receptor signaling through myeloid differentiation factor 88 maintains segregation between bacteria and the epithelium through production of anti-microbial proteins and inflammasome signaling mediates efficient goblet cell release of mucus containing granules. Conversely, negative regulators of TLR signaling help maintain a healthy microbiota resistant to pathogen infection. Methods to evade immune elimination by pathogens associated with human infections and inflammatory bowel disease are described. Emerging evidence that pattern recognition receptors can differentiate between commensals and pathogens will be examined. Summary The balance of innate signaling in the intestine is crucial to homeostasis: too little and bacteria can directly contact the epithelium, too much depletes the protective microbiota creating a niche for pathogens. Understanding the dynamic interaction between the immune system and the microbiota in a variety of infection and inflammation models will hopefully translate to new therapies. PMID:25426971

An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition.

PubMed

Lozano-Diez, Alicia; Zazo, Ruben; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin

2017-01-01

Language recognition systems based on bottleneck features have recently become the state-of-the-art in this research field, showing its success in the last Language Recognition Evaluation (LRE 2015) organized by NIST (U.S. National Institute of Standards and Technology). This type of system is based on a deep neural network (DNN) trained to discriminate between phonetic units, i.e. trained for the task of automatic speech recognition (ASR). This DNN aims to compress information in one of its layers, known as bottleneck (BN) layer, which is used to obtain a new frame representation of the audio signal. This representation has been proven to be useful for the task of language identification (LID). Thus, bottleneck features are used as input to the language recognition system, instead of a classical parameterization of the signal based on cepstral feature vectors such as MFCCs (Mel Frequency Cepstral Coefficients). Despite the success of this approach in language recognition, there is a lack of studies analyzing in a systematic way how the topology of the DNN influences the performance of bottleneck feature-based language recognition systems. In this work, we try to fill-in this gap, analyzing language recognition results with different topologies for the DNN used to extract the bottleneck features, comparing them and against a reference system based on a more classical cepstral representation of the input signal with a total variability model. This way, we obtain useful knowledge about how the DNN configuration influences bottleneck feature-based language recognition systems performance.

Automatic Speech Recognition from Neural Signals: A Focused Review.

PubMed

Herff, Christian; Schultz, Tanja

2016-01-01

Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.

10 CFR 431.21 - Procedures for recognition and withdrawal of recognition of accreditation bodies and...

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR CERTAIN COMMERCIAL AND INDUSTRIAL EQUIPMENT Electric... Assistant Secretary for Energy Efficiency and Renewable Energy, U.S. Department of Energy, Forrestal... comments in a written statement submitted to the Assistant Secretary for Energy Efficiency and Renewable...

10 CFR 431.20 - Department of Energy recognition of nationally recognized certification programs.

Code of Federal Regulations, 2010 CFR

2010-01-01

... certification programs. 431.20 Section 431.20 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY... Incorporated and Methods of Determining Efficiency § 431.20 Department of Energy recognition of nationally... similar procedures and methodologies for determining the energy efficiency of electric motors. It must...

Speech Recognition in Noise by Children with and without Dyslexia: How is it Related to Reading?

PubMed

Nittrouer, Susan; Krieg, Letitia M; Lowenstein, Joanna H

2018-06-01

Developmental dyslexia is commonly viewed as a phonological deficit that makes it difficult to decode written language. But children with dyslexia typically exhibit other problems, as well, including poor speech recognition in noise. The purpose of this study was to examine whether the speech-in-noise problems of children with dyslexia are related to their reading problems, and if so, if a common underlying factor might explain both. The specific hypothesis examined was that a spectral processing disorder results in these children receiving smeared signals, which could explain both the diminished sensitivity to phonological structure - leading to reading problems - and the speech recognition in noise difficulties. The alternative hypothesis tested in this study was that children with dyslexia simply have broadly based language deficits. Ninety-seven children between the ages of 7 years; 10 months and 12 years; 9 months participated: 46 with dyslexia and 51 without dyslexia. Children were tested on two dependent measures: word reading and recognition in noise with two types of sentence materials: as unprocessed (UP) signals, and as spectrally smeared (SM) signals. Data were collected for four predictor variables: phonological awareness, vocabulary, grammatical knowledge, and digit span. Children with dyslexia showed deficits on both dependent and all predictor variables. Their scores for speech recognition in noise were poorer than those of children without dyslexia for both the UP and SM signals, but by equivalent amounts across signal conditions indicating that they were not disproportionately hindered by spectral distortion. Correlation analyses on scores from children with dyslexia showed that reading ability and speech-in-noise recognition were only mildly correlated, and each skill was related to different underlying abilities. No substantial evidence was found to support the suggestion that the reading and speech recognition in noise problems of children with dyslexia arise from a single factor that could be defined as a spectral processing disorder. The reading and speech recognition in noise deficits of these children appeared to be largely independent. Copyright © 2018 Elsevier Ltd. All rights reserved.

Individual recognition between mother and infant bats (Myotis)

NASA Technical Reports Server (NTRS)

Turner, D.; Shaughnessy, A.; Gould, E.

1972-01-01

The recognition process and the basis for that recognition, in brown bats, between mother and infant are analyzed. Two parameters, ultrasonic communication and olfactory stimuli, are investigated. The test animals were not allowed any visual contact. It was concluded that individual recognition between mother and infant occurred. However, it could not be determined if the recognition was based on ultrasonic signals or olfactory stimuli.

Evolution of Novel Signal Traits in the Absence of Female Preferences in Neoconocephalus Katydids (Orthoptera, Tettigoniidae)

PubMed Central

Bush, Sarah L.; Schul, Johannes

2010-01-01

Background Significance Communication signals that function to bring together the sexes are important for maintaining reproductive isolation in many taxa. Changes in male calls are often attributed to sexual selection, in which female preferences initiate signal divergence. Natural selection can also influence signal traits if calls attract predators or parasitoids, or if calling is energetically costly. Neutral evolution is often neglected in the context of acoustic communication. Methodology/Principal Findings We describe a signal trait that appears to have evolved in the absence of either sexual or natural selection. In the katydid genus Neoconocephalus, calls with a derived pattern in which pulses are grouped into pairs have evolved five times independently. We have previously shown that in three of these species, females require the double pulse pattern for call recognition, and hence the recognition system of the females is also in a derived state. Here we describe the remaining two species and find that although males produce the derived call pattern, females use the ancestral recognition mechanism in which no pulse pattern is required. Females respond equally well to the single and double pulse calls, indicating that the derived trait is selectively neutral in the context of mate recognition. Conclusions/Significance These results suggest that 1) neutral changes in signal traits could be important in the diversification of communication systems, and 2) males rather than females may be responsible for initiating signal divergence. PMID:20805980

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin.

PubMed

Fuchs, Julian E; Huber, Roland G; Waldner, Birgit J; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R

2015-01-01

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm "dynamics govern specificity" might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design.

Estradiol-induced object recognition memory consolidation is dependent on activation of mTOR signaling in the dorsal hippocampus

PubMed Central

Fortress, Ashley M.; Fan, Lu; Orr, Patrick T.; Zhao, Zaorui; Frick, Karyn M.

2013-01-01

The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of object recognition memory consolidation produced by dorsal hippocampal infusion of 17β-estradiol (E2) is dependent on mTOR signaling in the dorsal hippocampus, and whether E2-induced mTOR signaling is dependent on dorsal hippocampal phosphatidylinositol 3-kinase (PI3K) and extracellular signal-regulated kinase (ERK) activation. We first demonstrated that the enhancement of object recognition induced by E2 was blocked by dorsal hippocampal inhibition of ERK, PI3K, or mTOR activation. We then showed that an increase in dorsal hippocampal ERK phosphorylation 5 min after intracerebroventricular (ICV) E2 infusion was also blocked by dorsal hippocampal infusion of the three cell signaling inhibitors. Next, we found that ICV infusion of E2 increased phosphorylation of the downstream mTOR targets S6K (Thr-421) and 4E-BP1 in the dorsal hippocampus 5 min after infusion, and that this phosphorylation was blocked by dorsal hippocampal infusion of inhibitors of ERK, PI3K, and mTOR. Collectively, these data demonstrate for the first time that activation of the dorsal hippocampal mTOR signaling pathway is necessary for E2 to enhance object recognition memory consolidation and that E2-induced mTOR activation is dependent on upstream activation of ERK and PI3K signaling. PMID:23422279

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

PubMed

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

PubMed Central

Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition. PMID:28356908

Type I Interferon Production Induced by Streptococcus pyogenes-Derived Nucleic Acids Is Required for Host Protection

PubMed Central

Gratz, Nina; Hartweger, Harald; Matt, Ulrich; Kratochvill, Franz; Janos, Marton; Sigel, Stefanie; Drobits, Barbara; Li, Xiao-Dong; Knapp, Sylvia; Kovarik, Pavel

2011-01-01

Streptococcus pyogenes is a Gram-positive human pathogen that is recognized by yet unknown pattern recognition receptors (PRRs). Engagement of these receptor molecules during infection with S. pyogenes, a largely extracellular bacterium with limited capacity for intracellular survival, causes innate immune cells to produce inflammatory mediators such as TNF, but also type I interferon (IFN). Here we show that signaling elicited by type I IFNs is required for successful defense of mice against lethal subcutaneous cellulitis caused by S. pyogenes. Type I IFN signaling was accompanied with reduced neutrophil recruitment to the site of infection. Mechanistic analysis revealed that macrophages and conventional dendritic cells (cDCs) employ different signaling pathways leading to IFN-beta production. Macrophages required IRF3, STING, TBK1 and partially MyD88, whereas in cDCs the IFN-beta production was fully dependent on IRF5 and MyD88. Furthermore, IFN-beta production by macrophages was dependent on the endosomal delivery of streptococcal DNA, while in cDCs streptococcal RNA was identified as the IFN-beta inducer. Despite a role of MyD88 in both cell types, the known IFN-inducing TLRs were individually not required for generation of the IFN-beta response. These results demonstrate that the innate immune system employs several strategies to efficiently recognize S. pyogenes, a pathogenic bacterium that succeeded in avoiding recognition by the standard arsenal of TLRs. PMID:21625574

Perceptual Plasticity for Auditory Object Recognition

PubMed Central

Heald, Shannon L. M.; Van Hedger, Stephen C.; Nusbaum, Howard C.

2017-01-01

In our auditory environment, we rarely experience the exact acoustic waveform twice. This is especially true for communicative signals that have meaning for listeners. In speech and music, the acoustic signal changes as a function of the talker (or instrument), speaking (or playing) rate, and room acoustics, to name a few factors. Yet, despite this acoustic variability, we are able to recognize a sentence or melody as the same across various kinds of acoustic inputs and determine meaning based on listening goals, expectations, context, and experience. The recognition process relates acoustic signals to prior experience despite variability in signal-relevant and signal-irrelevant acoustic properties, some of which could be considered as “noise” in service of a recognition goal. However, some acoustic variability, if systematic, is lawful and can be exploited by listeners to aid in recognition. Perceivable changes in systematic variability can herald a need for listeners to reorganize perception and reorient their attention to more immediately signal-relevant cues. This view is not incorporated currently in many extant theories of auditory perception, which traditionally reduce psychological or neural representations of perceptual objects and the processes that act on them to static entities. While this reduction is likely done for the sake of empirical tractability, such a reduction may seriously distort the perceptual process to be modeled. We argue that perceptual representations, as well as the processes underlying perception, are dynamically determined by an interaction between the uncertainty of the auditory signal and constraints of context. This suggests that the process of auditory recognition is highly context-dependent in that the identity of a given auditory object may be intrinsically tied to its preceding context. To argue for the flexible neural and psychological updating of sound-to-meaning mappings across speech and music, we draw upon examples of perceptual categories that are thought to be highly stable. This framework suggests that the process of auditory recognition cannot be divorced from the short-term context in which an auditory object is presented. Implications for auditory category acquisition and extant models of auditory perception, both cognitive and neural, are discussed. PMID:28588524

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

NASA Astrophysics Data System (ADS)

Su, Zhongqing; Ye, Lin

2004-08-01

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Modeling Fan Effects on the Time Course of Associative Recognition

ERIC Educational Resources Information Center

Schneider, Darryl W.; Anderson, John R.

2012-01-01

We investigated the time course of associative recognition using the response signal procedure, whereby a stimulus is presented and followed after a variable lag by a signal indicating that an immediate response is required. More specifically, we examined the effects of associative fan (the number of associations that an item has with other items…

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

PubMed

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

PubMed Central

Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Dissecting the Signaling Mechanisms Underlying Recognition and Preference of Food Odors

PubMed Central

Harris, Gareth; Shen, Yu; Ha, Heonick; Donato, Alessandra; Wallis, Samuel; Zhang, Xiaodong

2014-01-01

Food is critical for survival. Many animals, including the nematode Caenorhabditis elegans, use sensorimotor systems to detect and locate preferred food sources. However, the signaling mechanisms underlying food-choice behaviors are poorly understood. Here, we characterize the molecular signaling that regulates recognition and preference between different food odors in C. elegans. We show that the major olfactory sensory neurons, AWB and AWC, play essential roles in this behavior. A canonical Gα-protein, together with guanylate cyclases and cGMP-gated channels, is needed for the recognition of food odors. The food-odor-evoked signal is transmitted via glutamatergic neurotransmission from AWC and through AMPA and kainate-like glutamate receptor subunits. In contrast, peptidergic signaling is required to generate preference between different food odors while being dispensable for the recognition of the odors. We show that this regulation is achieved by the neuropeptide NLP-9 produced in AWB, which acts with its putative receptor NPR-18, and by the neuropeptide NLP-1 produced in AWC. In addition, another set of sensory neurons inhibits food-odor preference. These mechanistic logics, together with a previously mapped neural circuit underlying food-odor preference, provide a functional network linking sensory response, transduction, and downstream receptors to process complex olfactory information and generate the appropriate behavioral decision essential for survival. PMID:25009271

Character displacement of Cercopithecini primate visual signals

PubMed Central

Allen, William L.; Stevens, Martin; Higham, James P.

2014-01-01

Animal visual signals have the potential to act as an isolating barrier to prevent interbreeding of populations through a role in species recognition. Within communities of competing species, species recognition signals are predicted to undergo character displacement, becoming more visually distinctive from each other, however this pattern has rarely been identified. Using computational face recognition algorithms to model primate face processing, we demonstrate that the face patterns of guenons (tribe: Cercopithecini) have evolved under selection to become more visually distinctive from those of other guenon species with whom they are sympatric. The relationship between the appearances of sympatric species suggests that distinguishing conspecifics from other guenon species has been a major driver of diversification in guenon face appearance. Visual signals that have undergone character displacement may have had an important role in the tribe’s radiation, keeping populations that became geographically separated reproductively isolated on secondary contact. PMID:24967517

Pen-chant: Acoustic emissions of handwriting and drawing

NASA Astrophysics Data System (ADS)

Seniuk, Andrew G.

The sounds generated by a writing instrument ('pen-chant') provide a rich and underutilized source of information for pattern recognition. We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. We design and implement a family of recognizers using a template matching approach, with templates and similarity measures derived variously from: smoothed amplitude signal with fixed resolution, discrete sequence of magnitudes obtained from peaks in the smoothed amplitude signal, and ordered tree obtained from a scale space signal representation. Test results are presented for recognition of isolated lowercase cursive characters and for whole words. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. Our first set of results, using samples provided by the author, yield recognition rates of over 70% (alphabet) and 90% (26 words), with a confidence of +/-8%, based solely on acoustic emissions. Our second set of results uses data gathered from nine writers. These results demonstrate that acoustic emissions are a rich source of information, usable---on their own or in conjunction with image-based features---to solve pattern recognition problems. In future work, this approach can be applied to writer identification, handwriting and gesture-based computer input technology, emotion recognition, and temporal analysis of sketches.

Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

NASA Astrophysics Data System (ADS)

Kim, Jonghwa; André, Elisabeth

This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

Investigations of fluid-strain interaction using Plate Boundary Observatory borehole data

NASA Astrophysics Data System (ADS)

Boyd, Jeffrey Michael

Software has a great impact on the energy efficiency of any computing system--it can manage the components of a system efficiently or inefficiently. The impact of software is amplified in the context of a wearable computing system used for activity recognition. The design space this platform opens up is immense and encompasses sensors, feature calculations, activity classification algorithms, sleep schedules, and transmission protocols. Design choices in each of these areas impact energy use, overall accuracy, and usefulness of the system. This thesis explores methods software can influence the trade-off between energy consumption and system accuracy. In general the more energy a system consumes the more accurate will be. We explore how finding the transitions between human activities is able to reduce the energy consumption of such systems without reducing much accuracy. We introduce the Log-likelihood Ratio Test as a method to detect transitions, and explore how choices of sensor, feature calculations, and parameters concerning time segmentation affect the accuracy of this method. We discovered an approximate 5X increase in energy efficiency could be achieved with only a 5% decrease in accuracy. We also address how a system's sleep mode, in which the processor enters a low-power state and sensors are turned off, affects a wearable computing platform that does activity recognition. We discuss the energy trade-offs in each stage of the activity recognition process. We find that careful analysis of these parameters can result in great increases in energy efficiency if small compromises in overall accuracy can be tolerated. We call this the ``Great Compromise.'' We found a 6X increase in efficiency with a 7% decrease in accuracy. We then consider how wireless transmission of data affects the overall energy efficiency of a wearable computing platform. We find that design decisions such as feature calculations and grouping size have a great impact on the energy consumption of the system because of the amount of data that is stored and transmitted. For example, storing and transmitting vector-based features such as FFT or DCT do not compress the signal and would use more energy than storing and transmitting the raw signal. The effect of grouping size on energy consumption depends on the feature. For scalar features energy consumption is proportional in the inverse of grouping size, so it's reduced as grouping size goes up. For features that depend on the grouping size, such as FFT, energy increases with the logarithm of grouping size, so energy consumption increases slowly as grouping size increases. We find that compressing data through activity classification and transition detection significantly reduces energy consumption and that the energy consumed for the classification overhead is negligible compared to the energy savings from data compression. We provide mathematical models of energy usage and data generation, and test our ideas using a mobile computing platform, the Texas Instruments Chronos watch.

A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a Probabilistic Neural Network

PubMed Central

Xu, Jing; Wang, Zhongbin; Tan, Chao; Si, Lei; Liu, Xinhua

2015-01-01

In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method. PMID:26528985

Crystal Structure of the Complex of Human FasL and Its Decoy Receptor DcR3.

PubMed

Liu, Weifeng; Ramagopal, Udupi; Cheng, Huiyong; Bonanno, Jeffrey B; Toro, Rafael; Bhosle, Rahul; Zhan, Chenyang; Almo, Steven C

2016-11-01

The apoptotic effect of FasL:Fas signaling is disrupted by DcR3, a unique secreted member of the tumor necrosis factor receptor superfamily, which also binds and neutralizes TL1A and LIGHT. DcR3 is highly elevated in patients with various tumors and contributes to mechanisms by which tumor cells to evade host immune surveillance. Here we report the crystal structure of FasL in complex with DcR3. Comparison of FasL:DcR3 structure with our earlier TL1A:DcR3 and LIGHT:DcR3 structures supports a paradigm involving the recognition of invariant main-chain and conserved side-chain functionalities, which is responsible for the recognition of multiple TNF ligands exhibited by DcR3. The FasL:DcR3 structure also provides insight into the FasL:Fas recognition surface. We demonstrate that the ability of recombinant FasL to induce Jurkat cell apoptosis is significantly enhanced by native glycosylation or by structure-inspired mutations, both of which result in reduced tendency to aggregate. All of these activities are efficiently inhibited by recombinant DcR3. Copyright © 2016 Elsevier Ltd. All rights reserved.

An approach to emotion recognition in single-channel EEG signals: a mother child interaction

NASA Astrophysics Data System (ADS)

Gómez, A.; Quintero, L.; López, N.; Castro, J.

2016-04-01

In this work, we perform a first approach to emotion recognition from EEG single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are analyzed and processed using several window sizes by performing a statistical analysis over features in the time and frequency domains. Finally, a neural network obtained an average accuracy rate of 99% of classification in two emotional states such as happiness and sadness.

Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation

PubMed Central

Hao, Jiucang; Attias, Hagai; Nagarajan, Srikantan; Lee, Te-Won; Sejnowski, Terrence J.

2010-01-01

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback–Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation–maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion. PMID:20428253

Correlation dynamics and enhanced signals for the identification of serial biomolecules and DNA bases.

PubMed

Ahmed, Towfiq; Haraldsen, Jason T; Rehr, John J; Di Ventra, Massimiliano; Schuller, Ivan; Balatsky, Alexander V

2014-03-28

Nanopore-based sequencing has demonstrated a significant potential for the development of fast, accurate, and cost-efficient fingerprinting techniques for next generation molecular detection and sequencing. We propose a specific multilayered graphene-based nanopore device architecture for the recognition of single biomolecules. Molecular detection and analysis can be accomplished through the detection of transverse currents as the molecule or DNA base translocates through the nanopore. To increase the overall signal-to-noise ratio and the accuracy, we implement a new 'multi-point cross-correlation' technique for identification of DNA bases or other molecules on the single molecular level. We demonstrate that the cross-correlations between each nanopore will greatly enhance the transverse current signal for each molecule. We implement first-principles transport calculations for DNA bases surveyed across a multilayered graphene nanopore system to illustrate the advantages of the proposed geometry. A time-series analysis of the cross-correlation functions illustrates the potential of this method for enhancing the signal-to-noise ratio. This work constitutes a significant step forward in facilitating fingerprinting of single biomolecules using solid state technology.

Errare machinale est: the use of error-related potentials in brain-machine interfaces

PubMed Central

Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

2014-01-01

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches. PMID:25100937

Architecture of optical sensor for recognition of multiple toxic metal ions from water.

PubMed

Shenashen, M A; El-Safty, S A; Elshehy, E A

2013-09-15

Here, we designed novel optical sensor based on the wormhole hexagonal mesoporous core/multi-shell silica nanoparticles that enabled the selective recognition and removal of these extremely toxic metals from drinking water. The surface-coating process of a mesoporous core/double-shell silica platforms by several consequence decorations using a cationic surfactant with double alkyl tails (CS-DAT) and then a synthesized dicarboxylate 1,5-diphenyl-3-thiocarbazone (III) signaling probe enabled us to create a unique hierarchical multi-shell sensor. In this design, the high loading capacity and wrapping of the CS-DAT and III organic moieties could be achieved, leading to the formation of silica core with multi-shells that formed from double-silica, CS-DAT, and III dressing layers. In this sensing system, notable changes in color and reflectance intensity of the multi-shelled sensor for Cu(2+), Co(2+), Cd(2+), and Hg(2+) ions, were observed at pH 2, 8, 9.5 and 11.5, respectively. The multi-shelled sensor is added to enable accessibility for continuous monitoring of several different toxic metal ions and efficient multi-ion sensing and removal capabilities with respect to reversibility, selectivity, and signal stability. Copyright © 2013 Elsevier B.V. All rights reserved.

An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Bin

2004-03-01

The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

A novel method for real-time edge-enhancement and its application to pattern recognition

NASA Astrophysics Data System (ADS)

Ge, Huayong; Bai, Enjian; Fan, Hong

2010-11-01

The coupling gain coefficient g is redefined and deduced based on coupling theory, the variant of coupling gain coefficient g for different ΓL and r is analyzed. A new optical system is proposed for image edge-enhancement. It recycles the back signal to amplify the edge signal, which has the advantages of high throughput efficiency and brightness. The optical system is designed and built, and the edge-enhanced image of hand bone is captured electronically by CCD camera. The principle of optical correlation is demonstrated, 3-D correlation distribution of letter H with and without edge-enhancement is simulated, the discrimination capability Iac and the full-width at half maximum intensity (FWHM) are compared for two kinds of correlators. The analysis shows that edge-enhancement preprocessing can improve the performance of correlator effectively.

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.

PubMed

Khushaba, Rami N; Takruri, Maen; Miro, Jaime Valls; Kodagoda, Sarath

2014-07-01

Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). Copyright © 2014 Elsevier Ltd. All rights reserved.

Unveiling NIR Aza-Boron-Dipyrromethene (BODIPY) Dyes as Raman Probes: Surface-Enhanced Raman Scattering (SERS)-Guided Selective Detection and Imaging of Human Cancer Cells.

PubMed

Adarsh, Nagappanpillai; Ramya, Adukkadan N; Maiti, Kaustabh Kumar; Ramaiah, Danaboyina

2017-10-12

The development of new Raman reporters has attracted immense attention in diagnostic research based on surface enhanced Raman scattering (SERS) techniques, which is a well established method for ultrasensitive detection through molecular fingerprinting and imaging. Herein, for the first time, we report the unique and efficient Raman active features of the selected aza-BODIPY dyes 1-6. These distinctive attributes could be extended at the molecular level to allow detection through SERS upon adsorption onto nano-roughened gold surface. Among the newly revealed Raman reporters, the amino substituted derivative 4 showed high signal intensity at very low concentrations (ca. 0.4 μm for 4-Au). Interestingly, an efficient nanoprobe has been constructed by using gold nanoparticles as SERS substrate, and 4 as the Raman reporter (4-Au@PEG), which unexpectedly showed efficient recognition of three human cancer cells (lung: A549, cervical: HeLa, Fibrosarcoma: HT-1080) without any specific surface marker. We observed well reflected and resolved Raman mapping and characteristic signature peaks whereas, such recognition was not observed in normal fibroblast (3T3L1) cells. To confirm these findings, a SERS nanoprobe was conjugated with a specific tumour targeting marker, EGFR (Epidermal Growth Factor Receptor), a well known targeted agent for Human Fibrosarcoma (HT1080). This nanoprobe efficiently targeted the surface marker of HT1080 cells, threreby demonstrating its use as an ultrasensitive Raman probe for detection and targeted imaging, leaving normal cells unaffected. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

PubMed

Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

2017-07-03

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

Some-or-none recollection: Evidence from item and source memory.

PubMed

Onyper, Serge V; Zhang, Yaofei X; Howard, Marc W

2010-05-01

Dual-process theory hypothesizes that recognition memory depends on 2 distinguishable memory signals. Recollection reflects conscious recovery of detailed information about the learning episode. Familiarity reflects a memory signal that is not accompanied by a vivid conscious experience but nonetheless enables participants to distinguish recently experienced probe items from novel ones. This dual-process explanation of recognition memory has gained wide acceptance among cognitive neuroscientists and some cognitive psychologists. Nonetheless, its difficulty in providing a quantitatively satisfactory description of performance has precluded a consensus not only regarding the theoretical structure of recognition memory but also about how to best measure recognition accuracy. In 2 experiments we show that neither the standard formulation of dual-process signal detection (DPSD) theory nor a widely used single-process model called the unequal-variance signal-detection (UVSD) model provides a satisfactory explanation of recognition memory across different types of stimuli (words and travel scenes). In the variable-recollection dual-process (VRDP) model, recollection fails for some old probe items, as in standard formulations of DPSD, but gives rise to a continuous distribution of memory strengths when it succeeds. The VRDP can approximate both the DPSD and UVSD. In both experiments it provides a consistently superior fit across materials to the superset of the DPSD and UVSD. The VRDP offers a simple explanation of the form of conjoint item-source judgments, something neither the DPSD nor UVSD accomplishes. The success of the VRDP supports the core assumptions of dual-process theory by providing an excellent quantitative description of recognition performance across materials and response criteria.

Morphological and population genomic evidence that human faces have evolved to signal individual identity.

PubMed

Sheehan, Michael J; Nachman, Michael W

2014-09-16

Facial recognition plays a key role in human interactions, and there has been great interest in understanding the evolution of human abilities for individual recognition and tracking social relationships. Individual recognition requires sufficient cognitive abilities and phenotypic diversity within a population for discrimination to be possible. Despite the importance of facial recognition in humans, the evolution of facial identity has received little attention. Here we demonstrate that faces evolved to signal individual identity under negative frequency-dependent selection. Faces show elevated phenotypic variation and lower between-trait correlations compared with other traits. Regions surrounding face-associated single nucleotide polymorphisms show elevated diversity consistent with frequency-dependent selection. Genetic variation maintained by identity signalling tends to be shared across populations and, for some loci, predates the origin of Homo sapiens. Studies of human social evolution tend to emphasize cognitive adaptations, but we show that social evolution has shaped patterns of human phenotypic and genetic diversity as well.

Development of precursors recognition methods in vector signals

NASA Astrophysics Data System (ADS)

Kapralov, V. G.; Elagin, V. V.; Kaveeva, E. G.; Stankevich, L. A.; Dremin, M. M.; Krylov, S. V.; Borovov, A. E.; Harfush, H. A.; Sedov, K. S.

2017-10-01

Precursor recognition methods in vector signals of plasma diagnostics are presented. Their requirements and possible options for their development are considered. In particular, the variants of using symbolic regression for building a plasma disruption prediction system are discussed. The initial data preparation using correlation analysis and symbolic regression is discussed. Special attention is paid to the possibility of using algorithms in real time.

Estradiol-Induced Object Recognition Memory Consolidation Is Dependent on Activation of mTOR Signaling in the Dorsal Hippocampus

ERIC Educational Resources Information Center

Fortress, Ashley M.; Fan, Lu; Orr, Patrick T.; Zhao, Zaorui; Frick, Karyn M.

2013-01-01

The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of object recognition memory consolidation produced by dorsal hippocampal infusion of 17[Beta]-estradiol (E[subscript 2]) is dependent on mTOR…

STATUS OF THE SYSTEM OF SIGNALING PATTERN RECOGNITION RECEPTORS OF MONOCYTES AND GRANULOCYTES IN COSMONAUTS' PERIPHERAL BLOOD BEFORE AND AFTER LONG-DURATION MISSIONS TO THE INTERNATIONAL SPACE STATION.

PubMed

Ponomarev, S A; Berendeeva, T A; Kalinin, S A; Muranova, A V

The system of signaling pattern recognition receptors was studied in 8 cosmonauts aged 35 to 56 years before and after (R+) long-duration missions to the International space station. Peripheral blood samples were analyzed for the content of monocytes and granulocytes that express the signaling pattern recognition Toll- like (TLR) receptors localized as on cell surface (TLR1, TLR2, TLR4, TLR5, TLR6), so inside cells (TLR3, TLR8, TLR9). In parallel, serum concentrations of TLR2 (HSP60) and TLR4 ligands (HSP70, HMGB1) were measured. The results of investigations showed growth of HSP60, HSP70 and HMGB1 concentrations on R+1. In the;majority of cosmonauts increases in endogenous ligands were followed by growth in the number of both monocytes and granulocytes that express TLR2 1 TLR4. This consistency gives ground to assume that changes in the system of signaling pattern recognition receptors can stem .from the predominantly endogenous ligands' response to the effects of long-duration space flight on human organism.

Dynamics Govern Specificity of a Protein-Protein Interface: Substrate Recognition by Thrombin

PubMed Central

Fuchs, Julian E.; Huber, Roland G.; Waldner, Birgit J.; Kahler, Ursula; von Grafenstein, Susanne; Kramer, Christian; Liedl, Klaus R.

2015-01-01

Biomolecular recognition is crucial in cellular signal transduction. Signaling is mediated through molecular interactions at protein-protein interfaces. Still, specificity and promiscuity of protein-protein interfaces cannot be explained using simplistic static binding models. Our study rationalizes specificity of the prototypic protein-protein interface between thrombin and its peptide substrates relying solely on binding site dynamics derived from molecular dynamics simulations. We find conformational selection and thus dynamic contributions to be a key player in biomolecular recognition. Arising entropic contributions complement chemical intuition primarily reflecting enthalpic interaction patterns. The paradigm “dynamics govern specificity” might provide direct guidance for the identification of specific anchor points in biomolecular recognition processes and structure-based drug design. PMID:26496636

Self-organizing neural network models for visual pattern recognition.

PubMed

Fukushima, K

1987-01-01

Two neural network models for visual pattern recognition are discussed. The first model, called a "neocognitron", is a hierarchical multilayered network which has only afferent synaptic connections. It can acquire the ability to recognize patterns by "learning-without-a-teacher": the repeated presentation of a set of training patterns is sufficient, and no information about the categories of the patterns is necessary. The cells of the highest stage eventually become "gnostic cells", whose response shows the final result of the pattern-recognition of the network. Pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the stimulus pattern. The second model has not only afferent but also efferent synaptic connections, and is endowed with the function of selective attention. The afferent and the efferent signals interact with each other in the hierarchical network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent signals, and at the same time, the afferent signals gate efferent signal flow. When a complex figure, consisting of two patterns or more, is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the models is paying selective attention is affected by noise or defects, the model can "recall" the complete pattern from which the noise has been eliminated and the defects corrected.

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

NASA Astrophysics Data System (ADS)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase-only implementation with lower detection performance than full complex electronic systems. Our study includes pseudo-random pixel encoding techniques for approximating full complex filtering. Optical filter bank implementation is possible and they have the advantage of time averaging the entire filter bank at real time rates. Time-averaged optical filtering is computational comparable to billions of digital operations-per-second. For this reason, we believe future trends in high speed pattern recognition will involve hybrid architectures of both optical and DSP elements.

Rewriting nature's assembly manual for a ssRNA virus.

PubMed

Patel, Nikesh; Wroblewski, Emma; Leonov, German; Phillips, Simon E V; Tuma, Roman; Twarock, Reidun; Stockley, Peter G

2017-11-14

Satellite tobacco necrosis virus (STNV) is one of the smallest viruses known. Its genome encodes only its coat protein (CP) subunit, relying on the polymerase of its helper virus TNV for replication. The genome has been shown to contain a cryptic set of dispersed assembly signals in the form of stem-loops that each present a minimal CP-binding motif AXXA in the loops. The genomic fragment encompassing nucleotides 1-127 is predicted to contain five such packaging signals (PSs). We have used mutagenesis to determine the critical assembly features in this region. These include the CP-binding motif, the relative placement of PS stem-loops, their number, and their folding propensity. CP binding has an electrostatic contribution, but assembly nucleation is dominated by the recognition of the folded PSs in the RNA fragment. Mutation to remove all AXXA motifs in PSs throughout the genome yields an RNA that is unable to assemble efficiently. In contrast, when a synthetic 127-nt fragment encompassing improved PSs is swapped onto the RNA otherwise lacking CP recognition motifs, assembly is partially restored, although the virus-like particles created are incomplete, implying that PSs outside this region are required for correct assembly. Swapping this improved region into the wild-type STNV1 sequence results in a better assembly substrate than the viral RNA, producing complete capsids and outcompeting the wild-type genome in head-to-head competition. These data confirm details of the PS-mediated assembly mechanism for STNV and identify an efficient approach for production of stable virus-like particles encapsidating nonnative RNAs or other cargoes. Copyright © 2017 the Author(s). Published by PNAS.

Approximated mutual information training for speech recognition using myoelectric signals.

PubMed

Guo, Hua J; Chan, A D C

2006-01-01

A new training algorithm called the approximated maximum mutual information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

The recognition of extraterrestrial artificial signals

NASA Technical Reports Server (NTRS)

Seeger, C. L.

1980-01-01

Considerations in the design of receivers for the detection and recognition of artificial microwave signals of extraterrestrial origin are discussed. Following a review of the objectives of SETI and the probable reception and detection characteristics of extraterrestrial signals, means for the improvement of the sensitivity, signal-to-noise ratios and on-line data processing capabilities of SETI receivers are indicated. The characteristics of the signals likely to be present at the output of an ultra-low-noise microwave receiver are then examined, including the system background noise, terrestrial radiations, astrophysical radiations, accidental artificial radiations of terrestrial origin, and intentional radiations produced by humans and by extraterrestrial intelligence. The classes of extraterrestrial signals likely to be detected, beacons and leakage signals, are considered, and options in the specification of gating and thresholding for a high-spectral resolution, high-time-resolution signal discriminator are indicated. Possible tests for the nonhuman origin of a received signal are also pointed out.

An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal

PubMed Central

Ding, Huijun; He, Qing; Zhou, Yongjin; Dan, Guo; Cui, Song

2017-01-01

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results. PMID:29167655

Discriminative detection and enumeration of microbial life in marine subsurface sediments.

PubMed

Morono, Yuki; Terada, Takeshi; Masui, Noriaki; Inagaki, Fumio

2009-05-01

Detection and enumeration of microbial life in natural environments provide fundamental information about the extent of the biosphere on Earth. However, it has long been difficult to evaluate the abundance of microbial cells in sedimentary habitats because non-specific binding of fluorescent dye and/or auto-fluorescence from sediment particles strongly hampers the recognition of cell-derived signals. Here, we show a highly efficient and discriminative detection and enumeration technique for microbial cells in sediments using hydrofluoric acid (HF) treatment and automated fluorescent image analysis. Washing of sediment slurries with HF significantly reduced non-biological fluorescent signals such as amorphous silica and enhanced the efficiency of cell detachment from the particles. We found that cell-derived SYBR Green I signals can be distinguished from non-biological backgrounds by dividing green fluorescence (band-pass filter: 528/38 nm (center-wavelength/bandwidth)) by red (617/73 nm) per image. A newly developed automated microscope system could take a wide range of high-resolution image in a short time, and subsequently enumerate the accurate number of cell-derived signals by the calculation of green to red fluorescence signals per image. Using our technique, we evaluated the microbial population in deep marine sediments offshore Peru and Japan down to 365 m below the seafloor, which provided objective digital images as evidence for the quantification of the prevailing microbial life. Our method is hence useful to explore the extent of sub-seafloor life in the future scientific drilling, and moreover widely applicable in the study of microbial ecology.

cis-Acting elements important for retroviral RNA packaging specificity.

PubMed

Beasley, Benjamin E; Hu, Wei-Shau

2002-05-01

Spleen necrosis virus (SNV) proteins can package RNA from distantly related murine leukemia virus (MLV), whereas MLV proteins cannot package SNV RNA efficiently. We used this nonreciprocal recognition to investigate regions of packaging signals that influence viral RNA encapsidation specificity. Although the MLV and SNV packaging signals (Psi and E, respectively) do not contain significant sequence homology, they both contain a pair of hairpins. This hairpin pair was previously proposed to be the core element in MLV Psi. In the present study, MLV-based vectors were generated to contain chimeric SNV/MLV packaging signals in which the hairpins were replaced with the heterologous counterpart. The interactions between these chimeras and MLV or SNV proteins were examined by virus replication and RNA analyses. SNV proteins recognized all of the chimeras, indicating that these chimeras were functional. We found that replacing the hairpin pair did not drastically alter the ability of MLV proteins to package these chimeras. These results indicate that, despite the important role of the hairpin pair in RNA packaging, it is not the major motif responsible for the ability of MLV proteins to discriminate between the MLV and SNV packaging signals. To determine the role of sequences flanking the hairpins in RNA packaging specificity, vectors with swapped flanking regions were generated and evaluated. SNV proteins packaged all of these chimeras efficiently. In contrast, MLV proteins strongly favored chimeras with the MLV 5'-flanking regions. These data indicated that MLV Gag recognizes multiple elements in the viral packaging signal, including the hairpin structure and flanking regions.

A novel screen-printed mast cell-based electrochemical sensor for detecting spoilage bacterial quorum signaling molecules (N-acyl-homoserine-lactones) in freshwater fish.

PubMed

Jiang, Donglei; Liu, Yan; Jiang, Hui; Rao, Shengqi; Fang, Wu; Wu, Mangang; Yuan, Limin; Fang, Weiming

2018-04-15

A novel screen-printed cell-based electrochemical sensor was developed to assess bacterial quorum signaling molecules, N-acylhomoserine lactones (AHLs). Screen-printed carbon electrode (SPCE), which possesses excellent properties such as low-cost, disposable and energy-efficient, was modified with multi-walled carbon nanotubes (MWNTs) to improve electrochemical signals and enhance the sensitivity. Rat basophilic leukemia (RBL-2H3) mast cells encapsulated in alginate/graphene oxide (NaAgl/GO) hydrogel were immobilized on the MWNTs/SPCE to serve as recognition element. Electrochemical impedance spectroscopy (EIS) was employed to record the cell impedance signal as-influenced by Pseudomonas aeruginosa quorum-sensing molecule, N-3-oxododecanoyl homoserine lactone (3OC 12 -HSL). Experimental results show that 3OC 12 -HSL caused a significant decrease in cell viability in a dose dependent manner. The EIS value decreased with concentrations of 3OC 12 -HSL in the range of 0.1-1μM, and the detection limit for 3OC 12 -HSL was calculated to be 0.094μM. These results were confirmed via cell viability, SEM, TEM analysis. Next, the sensor was successfully applied to monitoring the production of AHLs by spoilage bacteria in three different freshwater fish juice samples which efficiently proved the practicability of this cell based method. Therefore, the proposed cell sensor may serve as an innovative and effective approach to the measurement of quorum signaling molecule and thus provides a new avenue for real-time monitoring the spoilage bacteria in freshwater fish production. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of Pre-Experimental Knowledge on Recognition Memory

ERIC Educational Resources Information Center

Bird, Chris M.; Davies, Rachel A.; Ward, Jamie; Burgess, Neil

2011-01-01

The influence of pre-experimental autobiographical knowledge on recognition memory was investigated using as memoranda faces that were either personally known or unknown to the participant. Under a dual process theory, such knowledge boosted both recollection- and familiarity-based recognition judgements. Under an unequal variance signal detection…

Are there differential deficits in facial emotion recognition between paranoid and non-paranoid schizophrenia? A signal detection analysis.

PubMed

Huang, Charles Lung-Cheng; Hsiao, Sigmund; Hwu, Hai-Gwo; Howng, Shen-Long

2013-10-30

This study assessed facial emotion recognition abilities in subjects with paranoid and non-paranoid schizophrenia (NPS) using signal detection theory. We explore the differential deficits in facial emotion recognition in 44 paranoid patients with schizophrenia (PS) and 30 non-paranoid patients with schizophrenia (NPS), compared to 80 healthy controls. We used morphed faces with different intensities of emotion and computed the sensitivity index (d') of each emotion. The results showed that performance differed between the schizophrenia and healthy controls groups in the recognition of both negative and positive affects. The PS group performed worse than the healthy controls group but better than the NPS group in overall performance. Performance differed between the NPS and healthy controls groups in the recognition of all basic emotions and neutral faces; between the PS and healthy controls groups in the recognition of angry faces; and between the PS and NPS groups in the recognition of happiness, anger, sadness, disgust, and neutral affects. The facial emotion recognition impairment in schizophrenia may reflect a generalized deficit rather than a negative-emotion specific deficit. The PS group performed worse than the control group, but better than the NPS group in facial expression recognition, with differential deficits between PS and NPS patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[Comigration of root nodule bacteria and bean plants to new habitats: coevolution mechanisms and practical importance (review)].

PubMed

Provorov, N A; Zhukov, V A; Kurchak, O N; Onishchuk, O P; Andronov, E E; Borisov, A Iu; Chizhevskaia, E P; Naumkina, T S; Ovtsyna, A O; Vorob'ev, N I; Simarov, B V; Tikhonovich, I A

2013-01-01

The review summarizes the results of studies on the comigration of tubercular bacteria and bean plants to new habitats, which is often accompanied by a decrease in the symbiosis efficiency due to a loss of the diversity of genes responsible for the interaction. This migration may lead to a rise in new symbionts as a result of gene transfers from initial symbionts to local bacteria. It was demonstrated that typically new symbionts lack an ability for N2 fixation but are highly competitive, blocking the inoculation of bean cultures by industrial strains. The design of coadapted systems of recognition and signal interaction of partners is a perspective approach to ensure competitive advantages of efficient rhizobia strains introduced into agrocenoses, together with host plants, over inactive local strains.

Dissecting the signaling mechanisms underlying recognition and preference of food odors.

PubMed

Harris, Gareth; Shen, Yu; Ha, Heonick; Donato, Alessandra; Wallis, Samuel; Zhang, Xiaodong; Zhang, Yun

2014-07-09

Food is critical for survival. Many animals, including the nematode Caenorhabditis elegans, use sensorimotor systems to detect and locate preferred food sources. However, the signaling mechanisms underlying food-choice behaviors are poorly understood. Here, we characterize the molecular signaling that regulates recognition and preference between different food odors in C. elegans. We show that the major olfactory sensory neurons, AWB and AWC, play essential roles in this behavior. A canonical Gα-protein, together with guanylate cyclases and cGMP-gated channels, is needed for the recognition of food odors. The food-odor-evoked signal is transmitted via glutamatergic neurotransmission from AWC and through AMPA and kainate-like glutamate receptor subunits. In contrast, peptidergic signaling is required to generate preference between different food odors while being dispensable for the recognition of the odors. We show that this regulation is achieved by the neuropeptide NLP-9 produced in AWB, which acts with its putative receptor NPR-18, and by the neuropeptide NLP-1 produced in AWC. In addition, another set of sensory neurons inhibits food-odor preference. These mechanistic logics, together with a previously mapped neural circuit underlying food-odor preference, provide a functional network linking sensory response, transduction, and downstream receptors to process complex olfactory information and generate the appropriate behavioral decision essential for survival. Copyright © 2014 the authors 0270-6474/14/339389-15$15.00/0.

Robust audio-visual speech recognition under noisy audio-video conditions.

PubMed

Stewart, Darryl; Seymour, Rowan; Pass, Adrian; Ming, Ji

2014-02-01

This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption. A significant advantage of MWSP is that it does not require any specific measurements of the signal in either stream to calculate appropriate stream weights during recognition, and as such it is modality-independent. This also means that MWSP complements and can be used alongside many of the other approaches that have been proposed in the literature for this problem. For evaluation we used the large XM2VTS database for speaker-independent audio-visual speech recognition. The extensive tests include both clean and corrupted utterances with corruption added in either/both the video and audio streams using a variety of types (e.g., MPEG-4 video compression) and levels of noise. The experiments show that this approach gives excellent performance in comparison to another well-known dynamic stream weighting approach and also compared to any fixed-weighted integration approach in both clean conditions or when noise is added to either stream. Furthermore, our experiments show that the MWSP approach dynamically selects suitable integration weights on a frame-by-frame basis according to the level of noise in the streams and also according to the naturally fluctuating relative reliability of the modalities even in clean conditions. The MWSP approach is shown to maintain robust recognition performance in all tested conditions, while requiring no prior knowledge about the type or level of noise.

Pattern-Recognition Algorithm for Locking Laser Frequency

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

2006-01-01

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

The Assessment of Recognition Memory Using the Remember/Know Procedure in Amnestic Mild Cognitive Impairment and Probable Alzheimer's Disease

ERIC Educational Resources Information Center

Hudon, Carol; Belleville, Sylvie; Gauthier, Serge

2009-01-01

This study used the Remember/Know (R/K) procedure combined with signal detection analyses to assess recognition memory in 20 elders with amnestic mild cognitive impairment (aMCI), 10 patients with probable Alzheimer's disease (AD) as well as matched healthy older adults. Signal detection analyses first indicated that aMCI and control participants…

Reflectance of vegetation, soil, and water

NASA Technical Reports Server (NTRS)

Wiegand, C. L. (Principal Investigator)

1973-01-01

The author has identified the following significant results. A ratio of MSS channels 5 and 7 (5/7) and 5 to 6 (5/6) signals resulted in a correct recognition of 86.9% of the members of representative crop and soil conditions, compared with recognitions of 60.0, 64.1, 74.1, and 81.4% for channels 4, 5, 6, and 7 taken individually. Based on this result a satellite channel ratio procedure has been developed that enhances line printer gray maps for more efficient experimental test site location in the CCT data. Because independent estimates are not available to judge acreage estmates derived from ERTS-1 data against, except for a few crops, an interpenetrating sample constituting 3.5% of the county is ground truthed periodically. The crop of land uses and their acreages, respectively, as estimated from the interpenetrating samples, are: cotton, 129, 714; sorghum, 182,783; mixed citrus, 53,954; oranges, 16,929; grapefruit, 13,863; rangeland, 137,845; and, improved pastures, 57.169.

Development of signal processing algorithms for ultrasonic detection of coal seam interfaces

NASA Technical Reports Server (NTRS)

Purcell, D. D.; Ben-Bassat, M.

1976-01-01

A pattern recognition system is presented for determining the thickness of coal remaining on the roof and floor of a coal seam. The system was developed to recognize reflected pulse echo signals that are generated by an acoustical transducer and reflected from the coal seam interface. The flexibility of the system, however, should enable it to identify pulse-echo signals generated by radar or other techniques. The main difference being the specific features extracted from the recorded data as a basis for pattern recognition.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

The time course of individual face recognition: A pattern analysis of ERP signals.

PubMed

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Diversity of Innate Immune Recognition Mechanism for Bacterial Polymeric meso-Diaminopimelic Acid-type Peptidoglycan in Insects

PubMed Central

Yu, Yang; Park, Ji-Won; Kwon, Hyun-Mi; Hwang, Hyun-Ok; Jang, In-Hwan; Masuda, Akiko; Kurokawa, Kenji; Nakayama, Hiroshi; Lee, Won-Jae; Dohmae, Naoshi; Zhang, Jinghai; Lee, Bok Luel

2010-01-01

In Drosophila, the synthesis of antimicrobial peptides in response to microbial infections is under the control of the Toll and immune deficiency (Imd) signaling pathway. The Toll signaling pathway responds mainly to the lysine-type peptidoglycan of Gram-positive bacteria and fungal β-1,3-glucan, whereas the Imd pathway responds to the meso-diaminopimelic acid (DAP)-type peptidoglycan of Gram-negative bacteria and certain Gram-positive bacilli. Recently we determined the activation mechanism of a Toll signaling pathway biochemically using a large beetle, Tenebrio molitor. However, DAP-type peptidoglycan recognition mechanism and its signaling pathway are still unclear in the fly and beetle. Here, we show that polymeric DAP-type peptidoglycan, but not its monomeric form, formed a complex with Tenebrio peptidoglycan recognition protein-SA, and this complex activated the three-step proteolytic cascade to produce processed Spätzle, a Toll receptor ligand, and induced Drosophila defensin-like antimicrobial peptide in Tenebrio larvae similarly to polymeric lysine-type peptidoglycan. Monomeric DAP-type peptidoglycan induced Drosophila diptericin-like antimicrobial peptide in Tenebrio hemocytes. In addition, both polymeric and monomeric DAP-type peptidoglycans induced expression of Tenebrio peptidoglycan recognition protein-SC2, which is DAP-type peptidoglycan-selective N-acetylmuramyl-l-alanine amidase that functions as a DAP-type peptidoglycan scavenger, appearing to function as a negative regulator of the DAP-type peptidoglycan signaling by cleaving DAP-type peptidoglycan in Tenebrio larvae. Taken together, these results demonstrate that molecular recognition mechanism for polymeric DAP-type peptidoglycan is different between Tenebrio larvae and Drosophila adults, providing biochemical evidences of biological diversity of innate immune responses in insects. PMID:20702416

Study on Impact Acoustic—Visual Sensor-Based Sorting of ELV Plastic Materials

PubMed Central

Huang, Jiu; Tian, Chuyuan; Ren, Jingwei; Bian, Zhengfu

2017-01-01

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles’ (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling. PMID:28594341

Study on Impact Acoustic-Visual Sensor-Based Sorting of ELV Plastic Materials.

PubMed

Huang, Jiu; Tian, Chuyuan; Ren, Jingwei; Bian, Zhengfu

2017-06-08

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles' (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.

Polarized release of T-cell-receptor-enriched microvesicles at the immunological synapse.

PubMed

Choudhuri, Kaushik; Llodrá, Jaime; Roth, Eric W; Tsai, Jones; Gordo, Susana; Wucherpfennig, Kai W; Kam, Lance C; Stokes, David L; Dustin, Michael L

2014-03-06

The recognition events that mediate adaptive cellular immunity and regulate antibody responses depend on intercellular contacts between T cells and antigen-presenting cells (APCs). T-cell signalling is initiated at these contacts when surface-expressed T-cell receptors (TCRs) recognize peptide fragments (antigens) of pathogens bound to major histocompatibility complex molecules (pMHC) on APCs. This, along with engagement of adhesion receptors, leads to the formation of a specialized junction between T cells and APCs, known as the immunological synapse, which mediates efficient delivery of effector molecules and intercellular signals across the synaptic cleft. T-cell recognition of pMHC and the adhesion ligand intercellular adhesion molecule-1 (ICAM-1) on supported planar bilayers recapitulates the domain organization of the immunological synapse, which is characterized by central accumulation of TCRs, adjacent to a secretory domain, both surrounded by an adhesive ring. Although accumulation of TCRs at the immunological synapse centre correlates with T-cell function, this domain is itself largely devoid of TCR signalling activity, and is characterized by an unexplained immobilization of TCR-pMHC complexes relative to the highly dynamic immunological synapse periphery. Here we show that centrally accumulated TCRs are located on the surface of extracellular microvesicles that bud at the immunological synapse centre. Tumour susceptibility gene 101 (TSG101) sorts TCRs for inclusion in microvesicles, whereas vacuolar protein sorting 4 (VPS4) mediates scission of microvesicles from the T-cell plasma membrane. The human immunodeficiency virus polyprotein Gag co-opts this process for budding of virus-like particles. B cells bearing cognate pMHC receive TCRs from T cells and initiate intracellular signals in response to isolated synaptic microvesicles. We conclude that the immunological synapse orchestrates TCR sorting and release in extracellular microvesicles. These microvesicles deliver transcellular signals across antigen-dependent synapses by engaging cognate pMHC on APCs.

Polarized release of T-cell-receptor-enriched microvesicles at the immunological synapse

NASA Astrophysics Data System (ADS)

Choudhuri, Kaushik; Llodrá, Jaime; Roth, Eric W.; Tsai, Jones; Gordo, Susana; Wucherpfennig, Kai W.; Kam, Lance C.; Stokes, David L.; Dustin, Michael L.

2014-03-01

The recognition events that mediate adaptive cellular immunity and regulate antibody responses depend on intercellular contacts between T cells and antigen-presenting cells (APCs). T-cell signalling is initiated at these contacts when surface-expressed T-cell receptors (TCRs) recognize peptide fragments (antigens) of pathogens bound to major histocompatibility complex molecules (pMHC) on APCs. This, along with engagement of adhesion receptors, leads to the formation of a specialized junction between T cells and APCs, known as the immunological synapse, which mediates efficient delivery of effector molecules and intercellular signals across the synaptic cleft. T-cell recognition of pMHC and the adhesion ligand intercellular adhesion molecule-1 (ICAM-1) on supported planar bilayers recapitulates the domain organization of the immunological synapse, which is characterized by central accumulation of TCRs, adjacent to a secretory domain, both surrounded by an adhesive ring. Although accumulation of TCRs at the immunological synapse centre correlates with T-cell function, this domain is itself largely devoid of TCR signalling activity, and is characterized by an unexplained immobilization of TCR-pMHC complexes relative to the highly dynamic immunological synapse periphery. Here we show that centrally accumulated TCRs are located on the surface of extracellular microvesicles that bud at the immunological synapse centre. Tumour susceptibility gene 101 (TSG101) sorts TCRs for inclusion in microvesicles, whereas vacuolar protein sorting 4 (VPS4) mediates scission of microvesicles from the T-cell plasma membrane. The human immunodeficiency virus polyprotein Gag co-opts this process for budding of virus-like particles. B cells bearing cognate pMHC receive TCRs from T cells and initiate intracellular signals in response to isolated synaptic microvesicles. We conclude that the immunological synapse orchestrates TCR sorting and release in extracellular microvesicles. These microvesicles deliver transcellular signals across antigen-dependent synapses by engaging cognate pMHC on APCs.

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

NASA Technical Reports Server (NTRS)

Huang, Yiteng; Chen, Jingdong; Chen, Shaoyan

2010-01-01

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

The role of airway macrophages in apoptotic cell clearance following acute and chronic lung inflammation.

PubMed

Grabiec, Aleksander M; Hussell, Tracy

2016-07-01

Acute and chronic inflammatory responses in the lung are associated with the accumulation of large quantities of immune and structural cells undergoing apoptosis, which need to be engulfed by phagocytes in a process called 'efferocytosis'. Apoptotic cell recognition and removal from the lung is mediated predominantly by airway macrophages, though immature dendritic cells and non-professional phagocytes, such as epithelial cells and mesenchymal cells, can also display this function. Efficient clearance of apoptotic cells from the airways is essential for successful resolution of inflammation and the return to lung homeostasis. Disruption of this process leads to secondary necrosis of accumulating apoptotic cells, release of necrotic cell debris and subsequent uncontrolled inflammatory activation of the innate immune system by the released 'damage associated molecular patterns' (DAMPS). To control the duration of the immune response and prevent autoimmune reactions, anti-inflammatory signalling cascades are initiated in the phagocyte upon apoptotic cell uptake, mediated by a range of receptors that recognise specific phospholipids or proteins externalised on, or secreted by, the apoptotic cell. However, prolonged activation of apoptotic cell recognition receptors, such as the family of receptor tyrosine kinases Tyro3, Axl and MerTK (TAM), may delay or prevent inflammatory responses to subsequent infections. In this review, we will discuss recent advances in our understanding of the mechanism controlling apoptotic cell recognition and removal from the lung in homeostasis and during inflammation, the contribution of defective efferocytosis to chronic inflammatory lung diseases, such as chronic obstructive pulmonary disease, asthma and cystic fibrosis, and implications of the signals triggered by apoptotic cells in the susceptibility to pulmonary microbial infections.

Magnesium modification up-regulates the bioactivity of bone morphogenetic protein-2 upon calcium phosphate cement via enhanced BMP receptor recognition and Smad signaling pathway.

PubMed

Ding, Sai; Zhang, Jing; Tian, Yu; Huang, Baolin; Yuan, Yuan; Liu, Changsheng

2016-09-01

Efficient presentation of growth factors is one of the great challenges in tissue engineering. In living systems, bioactive factors exist in soluble as well as in matrix-bound forms, both of which play an integral role in regulating cell behaviors. Herein, effect of magnesium on osteogenic bioactivity of recombinant human bone morphogenetic protein-2 (rhBMP-2) was investigated systematically with a series of Mg modified calcium phosphate cements (xMCPCs, x means the content of magnesium phosphate cement wt%) as matrix model. The results indicated that the MCPC, especially 5MCPC, could promote the rhBMP-2-induced in vitro osteogenic differentiation via Smad signaling of C2C12 cells. Further studies demonstrated that all MCPC substrates exhibited similar rhBMP-2 release rate and preserved comparable conformation and biological activity of the released rhBMP-2. Also, the ionic extracts of MCPC made little difference to the bioactivity of rhBMP-2, either in soluble or in matrix-bound forms. However, with the quartz crystal microbalance (QCM), we observed a noticeable enhancement of rhBMP-2 mass-uptake on 5MCPC as well as a better recognition of the bound rhBMP-2 to BMPR IA and BMPR II. In vivo results demonstrated a better bone regeneration capacity of 5MCPC/rhBMP-2. From the above, our results demonstrated that it was the Mg anchored on the underlying substrates that tailored the way of rhBMP-2 bound on MCPC, and thus facilitated the recognition of BMPRs to stimulate osteogenic differentiation. The study will guide the development of Mg-doped bioactive bone implants for tissue regeneration. Copyright © 2016 Elsevier B.V. All rights reserved.

Macroscopic ordering of helical pores for arraying guest molecules noncentrosymmetrically

PubMed Central

Li, Chunji; Cho, Joonil; Yamada, Kuniyo; Hashizume, Daisuke; Araoka, Fumito; Takezoe, Hideo; Aida, Takuzo; Ishida, Yasuhiro

2015-01-01

Helical nanostructures have attracted continuous attention, not only as media for chiral recognition and synthesis, but also as motifs for studying intriguing physical phenomena that never occur in centrosymmetric systems. To improve the quality of signals from these phenomena, which is a key issue for their further exploration, the most straightforward is the macroscopic orientation of helices. Here as a versatile scaffold to rationally construct this hardly accessible structure, we report a polymer framework with helical pores that unidirectionally orient over a large area (∼10 cm2). The framework, prepared by crosslinking a supramolecular liquid crystal preorganized in a magnetic field, is chemically robust, functionalized with carboxyl groups and capable of incorporating various basic or cationic guest molecules. When a nonlinear optical chromophore is incorporated in the framework, the resultant complex displays a markedly efficient nonlinear optical output, owing to the coherence of signals ensured by the macroscopically oriented helical structure. PMID:26416086

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals

PubMed Central

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-01-01

Background: This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. Methods: We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. Results: The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). Conclusion: This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals. PMID:26622979

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals.

PubMed

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-07-06

This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

The integration of familiarity and recollection information in short-term recognition: modeling speed-accuracy trade-off functions.

PubMed

Göthe, Katrin; Oberauer, Klaus

2008-05-01

Dual process models postulate familiarity and recollection as the basis of the recognition process. We investigated the time-course of integration of the two information sources to one recognition judgment in a working memory task. We tested 24 subjects with a response signal variant of the modified Sternberg recognition task (Oberauer, 2001) to isolate the time course of three different probe types indicating different combinations of familiarity and source information. We compared two mathematical models implementing different ways of integrating familiarity and recollection. Within each model, we tested three assumptions about the nature of the familiarity signal, with familiarity having (a) only positive values, indicating similarity of the probe with the memory list, (b) only negative values, indicating novelty, or (c) both positive and negative values. Both models provided good fits to the data. A model combining the outputs of both processes additively (Integration Model) gave an overall better fit to the data than a model based on a continuous familiarity signal and a probabilistic all-or-none recollection process (Dominance Model).

Discrete-State and Continuous Models of Recognition Memory: Testing Core Properties under Minimal Assumptions

ERIC Educational Resources Information Center

Kellen, David; Klauer, Karl Christoph

2014-01-01

A classic discussion in the recognition-memory literature concerns the question of whether recognition judgments are better described by continuous or discrete processes. These two hypotheses are instantiated by the signal detection theory model (SDT) and the 2-high-threshold model, respectively. Their comparison has almost invariably relied on…

Testing Theories of Recognition Memory by Predicting Performance Across Paradigms

ERIC Educational Resources Information Center

Smith, David G.; Duncan, Matthew J. J.

2004-01-01

Signal-detection theory (SDT) accounts of recognition judgments depend on the assumption that recognition decisions result from a single familiarity-based process. However, fits of a hybrid SDT model, called dual-process theory (DPT), have provided evidence for the existence of a second, recollection-based process. In 2 experiments, the authors…

Signal detection with criterion noise: applications to recognition memory.

PubMed

Benjamin, Aaron S; Diaz, Michael; Wee, Serena

2009-01-01

A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of discrimination incorporating criterion variability are derived, and the model's relationship with extant models of decision making in discrimination tasks is examined. An experiment evaluating recognition memory for ensembles of word stimuli revealed that criterion noise is not trivial in magnitude and contributes substantially to variance in the slope of the isosensitivity function. The authors discuss how ND-TSD can help explain a number of current and historical puzzles in recognition memory, including the inconsistent relationship between manipulations of learning and the isosensitivity function's slope, the lack of invariance of the slope with manipulations of bias or payoffs, the effects of aging on the decision-making process in recognition, and the nature of responding in remember-know decision tasks. ND-TSD poses novel, theoretically meaningful constraints on theories of recognition and decision making more generally, and provides a mechanism for rapprochement between theories of decision making that employ deterministic response rules and those that postulate probabilistic response rules.

SSVEP recognition using common feature analysis in brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-04-15

Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer.

PubMed

Liu, Maolin; Li, Huaiyu; Wang, Yuan; Li, Fei; Chen, Xiuwan

2018-04-01

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer's application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Polydopamine nanotube mediated fluorescent biosensor for Hg(ii) determination through exonuclease III-assisted signal amplification.

PubMed

A, Ravikumar; P, Panneerselvam

2018-05-29

We describe a highly sensitive fluorescence biosensor incorporating polydopamine nanotubes (PDNTs) based on the mechanism of exonuclease III (Exo III) assisted signal amplification for the determination of Hg2+ in aqueous solution. Fluorescent probes of FAM labeled ssDNA (FAM-ssDNA) adsorbed on the PDNTs act as an efficient quencher. In the presence of Hg2+, the FAM-ssDNA can bind to Hg2+ to form double stranded DNA (dsDNA) via the formation of T-Hg2+-T base pairs. Then, the dsDNA was removed from the surface of the PDNTs to restore the fluorescence. The release of the dsDNA was triggered by Exo III digestion. At the same time, the liberated Hg2+ mediates a new cycle of digestion. This assay is ultrasensitive for the selective recognition of Hg2+, and a detection limit as low as 10 pM was achieved. In addition, the fluorescent biosensing system also displays remarkable specificity to Hg2+ in the presence of other possible competing ions. This approach was applied to the determination of Hg2+ in real water samples with good recovery and high efficiency for practical analysis.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

Toll-like receptors (TLRs) and immune disorders.

PubMed

Akashi-Takamura, Sachiko; Miyake, Kensuke

2006-10-01

Upon the invasion of pathogens, the immune system needs to mount defense responses immediately. Over the past 10 years, Toll-like receptors (TLRs) have been discovered in mammals and defined as pathogen sensors. TLRs are considered to bind directly to ligands, discriminate them immediately, and induce defense responses when appropriate. We here review microbial recognition by TLRs, downstream signaling, and the relationship of TLRs to susceptibility to infectious diseases and immune disorders. Recent reports have revealed a requirement for co-receptors in TLR responses. A TLR signaling pathway is required for protection against infectious diseases, but excessive signaling may lead to allergies, autoimmune diseases, or atherosclerosis. In humans, several deficiencies of signaling molecules downstream of TLRs, and TLR polymorphisms that affect recognition or signaling, were reported to cause immunodeficiencies. It is important to understand how TLR signaling is controlled.

Extracting fingerprint of wireless devices based on phase noise and multiple level wavelet decomposition

NASA Astrophysics Data System (ADS)

Zhao, Weichen; Sun, Zhuo; Kong, Song

2016-10-01

Wireless devices can be identified by the fingerprint extracted from the signal transmitted, which is useful in wireless communication security and other fields. This paper presents a method that extracts fingerprint based on phase noise of signal and multiple level wavelet decomposition. The phase of signal will be extracted first and then decomposed by multiple level wavelet decomposition. The statistic value of each wavelet coefficient vector is utilized for constructing fingerprint. Besides, the relationship between wavelet decomposition level and recognition accuracy is simulated. And advertised decomposition level is revealed as well. Compared with previous methods, our method is simpler and the accuracy of recognition remains high when Signal Noise Ratio (SNR) is low.

Response-related fMRI of veridical and false recognition of words.

PubMed

Heun, Reinhard; Jessen, Frank; Klose, Uwe; Erb, Michael; Granath, Dirk-Oliver; Grodd, Wolfgang

2004-02-01

Studies on the relation between local cerebral activation and retrieval success usually compared high and low performance conditions, and thus showed performance-related activation of different brain areas. Only a few studies directly compared signal intensities of different response categories during retrieval. During verbal recognition, we recently observed increased parieto-occipital activation related to false alarms. The present study intends to replicate and extend this observation by investigating common and differential activation by veridical and false recognition. Fifteen healthy volunteers performed a verbal recognition paradigm using 160 learned target and 160 new distractor words. The subjects had to indicate whether they had learned the word before or not. Echo-planar MRI of blood-oxygen-level-dependent signal changes was performed during this recognition task. Words were classified post hoc according to the subjects' responses, i.e. hits, false alarms, correct rejections and misses. Response-related fMRI-analysis was used to compare activation associated with the subjects' recognition success, i.e. signal intensities related to the presentation of words were compared by the above-mentioned four response types. During recognition, all word categories showed increased bilateral activation of the inferior frontal gyrus, the inferior temporal gyrus, the occipital lobe and the brainstem in comparison with the control condition. Hits and false alarms activated several areas including the left medial and lateral parieto-occipital cortex in comparison with subjectively unknown items, i.e. correct rejections and misses. Hits showed more pronounced activation in the medial, false alarms in the lateral parts of the left parieto-occipital cortex. Veridical and false recognition show common as well as different areas of cerebral activation in the left parieto-occipital lobe: increased activation of the medial parietal cortex by hits may correspond to true recognition, increased activation of the parieto-occipital cortex by false alarms may correspond to familiarity decisions. Further studies are needed to investigate the reasons for false decisions in healthy subjects and patients with memory problems.

Between Efficiency, Capability and Recognition: Competing Epistemes in Global Governance Reforms

ERIC Educational Resources Information Center

Chan, Jennifer

2007-01-01

This article examines global governance reforms as a site of contestation between three different "truths"/epistemes (the market, human rights principles, and cultural identity) in terms of the competing principles of efficiency, capability, and recognition. Nancy Fraser's conceptions of participation parity and a dialogical approach of…

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Virus recognition by Toll-7 activates antiviral autophagy in Drosophila.

PubMed

Nakamoto, Margaret; Moy, Ryan H; Xu, Jie; Bambina, Shelly; Yasunaga, Ari; Shelly, Spencer S; Gold, Beth; Cherry, Sara

2012-04-20

Innate immunity is highly conserved and relies on pattern recognition receptors (PRRs) such as Toll-like receptors (identified through their homology to Drosophila Toll) for pathogen recognition. Although Drosophila Toll is vital for immune recognition and defense, roles for the other eight Drosophila Tolls in immunity have remained elusive. Here we have shown that Toll-7 is a PRR both in vitro and in adult flies; loss of Toll-7 led to increased vesicular stomatitis virus (VSV) replication and mortality. Toll-7, along with additional uncharacterized Drosophila Tolls, was transcriptionally induced by VSV infection. Furthermore, Toll-7 interacted with VSV at the plasma membrane and induced antiviral autophagy independently of the canonical Toll signaling pathway. These data uncover an evolutionarily conserved role for a second Drosophila Toll receptor that links viral recognition to autophagy and defense and suggest that other Drosophila Tolls may restrict specific as yet untested pathogens, perhaps via noncanonical signaling pathways. Copyright © 2012 Elsevier Inc. All rights reserved.

Optimization of Light-Harvesting Pigment Improves Photosynthetic Efficiency.

PubMed

Jin, Honglei; Li, Mengshu; Duan, Sujuan; Fu, Mei; Dong, Xiaoxiao; Liu, Bing; Feng, Dongru; Wang, Jinfa; Wang, Hong-Bin

2016-11-01

Maximizing light capture by light-harvesting pigment optimization represents an attractive but challenging strategy to improve photosynthetic efficiency. Here, we report that loss of a previously uncharacterized gene, HIGH PHOTOSYNTHETIC EFFICIENCY1 (HPE1), optimizes light-harvesting pigments, leading to improved photosynthetic efficiency and biomass production. Arabidopsis (Arabidopsis thaliana) hpe1 mutants show faster electron transport and increased contents of carbohydrates. HPE1 encodes a chloroplast protein containing an RNA recognition motif that directly associates with and regulates the splicing of target RNAs of plastid genes. HPE1 also interacts with other plastid RNA-splicing factors, including CAF1 and OTP51, which share common targets with HPE1. Deficiency of HPE1 alters the expression of nucleus-encoded chlorophyll-related genes, probably through plastid-to-nucleus signaling, causing decreased total content of chlorophyll (a+b) in a limited range but increased chlorophyll a/b ratio. Interestingly, this adjustment of light-harvesting pigment reduces antenna size, improves light capture, decreases energy loss, mitigates photodamage, and enhances photosynthetic quantum yield during photosynthesis. Our findings suggest a novel strategy to optimize light-harvesting pigments that improves photosynthetic efficiency and biomass production in higher plants. © 2016 American Society of Plant Biologists. All Rights Reserved.

Optimization of Light-Harvesting Pigment Improves Photosynthetic Efficiency1[OPEN

PubMed Central

Jin, Honglei; Li, Mengshu; Duan, Sujuan; Fu, Mei; Dong, Xiaoxiao; Feng, Dongru; Wang, Jinfa

2016-01-01

Maximizing light capture by light-harvesting pigment optimization represents an attractive but challenging strategy to improve photosynthetic efficiency. Here, we report that loss of a previously uncharacterized gene, HIGH PHOTOSYNTHETIC EFFICIENCY1 (HPE1), optimizes light-harvesting pigments, leading to improved photosynthetic efficiency and biomass production. Arabidopsis (Arabidopsis thaliana) hpe1 mutants show faster electron transport and increased contents of carbohydrates. HPE1 encodes a chloroplast protein containing an RNA recognition motif that directly associates with and regulates the splicing of target RNAs of plastid genes. HPE1 also interacts with other plastid RNA-splicing factors, including CAF1 and OTP51, which share common targets with HPE1. Deficiency of HPE1 alters the expression of nucleus-encoded chlorophyll-related genes, probably through plastid-to-nucleus signaling, causing decreased total content of chlorophyll (a+b) in a limited range but increased chlorophyll a/b ratio. Interestingly, this adjustment of light-harvesting pigment reduces antenna size, improves light capture, decreases energy loss, mitigates photodamage, and enhances photosynthetic quantum yield during photosynthesis. Our findings suggest a novel strategy to optimize light-harvesting pigments that improves photosynthetic efficiency and biomass production in higher plants. PMID:27609860

Goldstone field test activities: Target search

NASA Technical Reports Server (NTRS)

Tarter, J.

1986-01-01

In March of this year prototype SETI equipment was installed at DSS13, the 26 meter research and development antenna at NASA's Goldstone complex of satellite tracking dishes. The SETI equipment will remain at this site at least through the end of the summer so that the hardware and software developed for signal detection and recognition can be fully tested in a dynamic observatory environment. The field tests are expected to help understand which strategies for observing and which signal recognition algorithms perform best in the presence of strong man-made interfering signals (RFI) and natural astronomical sources.

Pathogenicity and infection strategies of the fire blight pathogen Erwinia amylovora in Rosaceae: state of the art.

PubMed

Vrancken, K; Holtappels, M; Schoofs, H; Deckers, T; Valcke, R

2013-05-01

Plants are host to a large amount of pathogenic bacteria. Fire blight, caused by the bacterium Erwinia amylovora, is an important disease in Rosaceae. Pathogenicity of E. amylovora is greatly influenced by the production of exopolysaccharides, such as amylovoran, and the use of the type III secretion system, which enables bacteria to penetrate host tissue and cause disease. When infection takes place, plants have to rely on the ability of each cell to recognize the pathogen and the signals emanating from the infection site in order to generate several defence mechanisms. These mechanisms consist of physical barriers and the production of antimicrobial components, both in a preformed and an inducible manner. Inducible defence responses are activated upon the recognition of elicitor molecules by plant cell receptors, either derived from invading micro-organisms or from pathogen-induced degradation of plant tissue. This recognition event triggers a signal transduction cascade, leading to a range of defence responses [reactive oxygen species (ROS), plant hormones, secondary metabolites, …] and redeployment of cellular energy in a fast, efficient and multiresponsive manner, which prevents further pathogen ingress. This review highlights the research that has been performed during recent years regarding this specific plant-pathogen interaction between Erwinia amylovora and Rosaceae, with a special emphasis on the pathogenicity and the infection strategy of E. amylovora and the possible defence mechanisms of the plant against this disease.

Using Pattern Recognition and Discriminance Analysis to Predict Critical Events in Large Signal Databases

NASA Astrophysics Data System (ADS)

Feller, Jens; Feller, Sebastian; Mauersberg, Bernhard; Mergenthaler, Wolfgang

2009-09-01

Many applications in plant management require close monitoring of equipment performance, in particular with the objective to prevent certain critical events. At each point in time, the information available to classify the criticality of the process, is represented through the historic signal database as well as the actual measurement. This paper presents an approach to detect and predict critical events, based on pattern recognition and discriminance analysis.

The structure of Escherichia coli signal recognition particle revealed by scanning transmission electron microscopy.

PubMed

Mainprize, Iain L; Beniac, Daniel R; Falkovskaia, Elena; Cleverley, Robert M; Gierasch, Lila M; Ottensmeyer, F Peter; Andrews, David W

2006-12-01

Structural studies on various domains of the ribonucleoprotein signal recognition particle (SRP) have not converged on a single complete structure of bacterial SRP consistent with the biochemistry of the particle. We obtained a three-dimensional structure for Escherichia coli SRP by cryoscanning transmission electron microscopy and mapped the internal RNA by electron spectroscopic imaging. Crystallographic data were fit into the SRP reconstruction, and although the resulting model differed from previous models, they could be rationalized by movement through an interdomain linker of Ffh, the protein component of SRP. Fluorescence resonance energy transfer experiments determined interdomain distances that were consistent with our model of SRP. Docking our model onto the bacterial ribosome suggests a mechanism for signal recognition involving interdomain movement of Ffh into and out of the nascent chain exit site and suggests how SRP could interact and/or compete with the ribosome-bound chaperone, trigger factor, for a nascent chain during translation.

A Data Hiding Technique to Synchronously Embed Physiological Signals in H.264/AVC Encoded Video for Medicine Healthcare.

PubMed

Peña, Raul; Ávila, Alfonso; Muñoz, David; Lavariega, Juan

2015-01-01

The recognition of clinical manifestations in both video images and physiological-signal waveforms is an important aid to improve the safety and effectiveness in medical care. Physicians can rely on video-waveform (VW) observations to recognize difficult-to-spot signs and symptoms. The VW observations can also reduce the number of false positive incidents and expand the recognition coverage to abnormal health conditions. The synchronization between the video images and the physiological-signal waveforms is fundamental for the successful recognition of the clinical manifestations. The use of conventional equipment to synchronously acquire and display the video-waveform information involves complex tasks such as the video capture/compression, the acquisition/compression of each physiological signal, and the video-waveform synchronization based on timestamps. This paper introduces a data hiding technique capable of both enabling embedding channels and synchronously hiding samples of physiological signals into encoded video sequences. Our data hiding technique offers large data capacity and simplifies the complexity of the video-waveform acquisition and reproduction. The experimental results revealed successful embedding and full restoration of signal's samples. Our results also demonstrated a small distortion in the video objective quality, a small increment in bit-rate, and embedded cost savings of -2.6196% for high and medium motion video sequences.

Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

NASA Astrophysics Data System (ADS)

Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

2017-01-01

People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

Prevalence of impaired memory in hospitalized adults and associations with in-hospital sleep loss.

PubMed

Calev, Hila; Spampinato, Lisa M; Press, Valerie G; Meltzer, David O; Arora, Vineet M

2015-07-01

Effective inpatient teaching requires intact patient memory, but studies suggest hospitalized adults may have memory deficits. Sleep loss among inpatients could contribute to memory impairment. To assess memory in older hospitalized adults, and to test the association between sleep quantity, sleep quality, and memory, in order to identify a possible contributor to memory deficits in these patients. Prospective cohort study. General medicine and hematology/oncology inpatient wards. Fifty-nine hospitalized adults at least 50 years of age with no diagnosed sleep disorder. Immediate memory and memory after a 24-hour delay were assessed using a word recall and word recognition task from the University of Southern California Repeatable Episodic Memory Test. A vignette-based memory task was piloted as an alternative test more closely resembling discharge instructions. Sleep duration and efficiency overnight in the hospital were measured using actigraphy. Mean immediate recall was 3.8 words out of 15 (standard deviation = 2.1). Forty-nine percent of subjects had poor memory, defined as immediate recall score of 3 or lower. Median immediate recognition was 11 words out of 15 (interquartile range [IQR] = 9-13). Median delayed recall score was 1 word, and median delayed recognition was 10 words (IQR = 8-12). In-hospital sleep duration and efficiency were not significantly associated with memory. The medical vignette score was correlated with immediate recall (r = 0.49, P < 0.01). About half of the inpatients studied had poor memory while in the hospital, signaling that hospitalization might not be an ideal teachable moment. In-hospital sleep was not associated with memory scores. © 2015 Society of Hospital Medicine.

Prevalence of Impaired Memory in Hospitalized Adults and Associations with In-Hospital Sleep Loss

PubMed Central

Calev, Hila; Spampinato, Lisa M; Press, Valerie G; Meltzer, David O; Arora, Vineet M

2015-01-01

Background Effective inpatient teaching requires intact patient memory, but studies suggest hospitalized adults may have memory deficits. Sleep loss among inpatients could contribute to memory impairment. Objective To assess memory in older hospitalized adults, and to test the association between sleep quantity, sleep quality and memory, in order to identify a possible contributor to memory deficits in these patients. Design Prospective cohort study Setting General medicine and hematology/oncology inpatient wards Patients 59 hospitalized adults at least 50 years of age with no diagnosed sleep disorder. Measurements Immediate memory and memory after a 24-hour delay were assessed using a word recall and word recognition task from the University of Southern California Repeatable Episodic Memory Test (USC-REMT). A vignette-based memory task was piloted as an alternative test more closely resembling discharge instructions. Sleep duration and efficiency overnight in the hospital were measured using actigraphy. Results Mean immediate recall was 3.8 words out of 15 (SD=2.1). Forty-nine percent of subjects had poor memory, defined as immediate recall score of 3 or lower. Median immediate recognition was 11 words out of 15 (IQR=9, 13). Median delayed recall score was 1 word and median delayed recognition was 10 words (IQR= 8–12). In-hospital sleep duration and efficiency were not significantly associated with memory. The medical vignette score was correlated with immediate recall (r=0.49, p<0.01) Conclusions About half of inpatients studied had poor memory while in the hospital, signaling that hospitalization might not be an ideal teachable moment. In-hospital sleep was not associated with memory scores. PMID:25872763

Microdomains in the membrane landscape shape antigen-presenting cell function.

PubMed

Zuidscherwoude, Malou; de Winde, Charlotte M; Cambi, Alessandra; van Spriel, Annemiek B

2014-02-01

The plasma membrane of immune cells is a highly organized cell structure that is key to the initiation and regulation of innate and adaptive immune responses. It is well-established that immunoreceptors embedded in the plasma membrane have a nonrandom spatial distribution that is important for coupling to components of intracellular signaling cascades. In the last two decades, specialized membrane microdomains, including lipid rafts and TEMs, have been identified. These domains are preformed structures ("physical entities") that compartmentalize proteins, lipids, and signaling molecules into multimolecular assemblies. In APCs, different microdomains containing immunoreceptors (MHC proteins, PRRs, integrins, among others) have been reported that are imperative for efficient pathogen recognition, the formation of the immunological synapse, and subsequent T cell activation. In addition, recent work has demonstrated that tetraspanin microdomains and lipid rafts are involved in BCR signaling and B cell activation. Research into the molecular mechanisms underlying membrane domain formation is fundamental to a comprehensive understanding of membrane-proximal signaling and APC function. This review will also discuss the advances in the microscopy field for the visualization of the plasma membrane, as well as the recent progress in targeting microdomains as novel, therapeutic approach for infectious and malignant diseases.

Do not let death do us part: 'find-me' signals in communication between dying cells and the phagocytes.

PubMed

Medina, C B; Ravichandran, K S

2016-06-01

The turnover and clearance of cells is an essential process that is part of many physiological and pathological processes. Improper or deficient clearance of apoptotic cells can lead to excessive inflammation and autoimmune disease. The steps involved in cell clearance include: migration of the phagocyte toward the proximity of the dying cells, specific recognition and internalization of the dying cell, and degradation of the corpse. The ability of phagocytes to recognize and react to dying cells to perform efficient and immunologically silent engulfment has been well-characterized in vitro and in vivo. However, how apoptotic cells themselves initiate the corpse removal and also influence the cells within the neighboring environment during clearance was less understood. Recent exciting observations suggest that apoptotic cells can attract phagocytes through the regulated release of 'find-me' signals. More recent studies also suggest that these find-me signals can have additional roles outside of phagocyte attraction to help orchestrate engulfment. This review will discuss our current understanding of the different find-me signals released by apoptotic cells, how they may be relevant in vivo, and their additional roles in facilitating engulfment.

Do not let death do us part: ‘find-me' signals in communication between dying cells and the phagocytes

PubMed Central

Medina, C B; Ravichandran, K S

2016-01-01

The turnover and clearance of cells is an essential process that is part of many physiological and pathological processes. Improper or deficient clearance of apoptotic cells can lead to excessive inflammation and autoimmune disease. The steps involved in cell clearance include: migration of the phagocyte toward the proximity of the dying cells, specific recognition and internalization of the dying cell, and degradation of the corpse. The ability of phagocytes to recognize and react to dying cells to perform efficient and immunologically silent engulfment has been well-characterized in vitro and in vivo. However, how apoptotic cells themselves initiate the corpse removal and also influence the cells within the neighboring environment during clearance was less understood. Recent exciting observations suggest that apoptotic cells can attract phagocytes through the regulated release of ‘find-me' signals. More recent studies also suggest that these find-me signals can have additional roles outside of phagocyte attraction to help orchestrate engulfment. This review will discuss our current understanding of the different find-me signals released by apoptotic cells, how they may be relevant in vivo, and their additional roles in facilitating engulfment. PMID:26891690

ATP-independent reversal of a membrane protein aggregate by a chloroplast SRP

PubMed Central

Jaru-Ampornpan, Peera; Shen, Kuang; Lam, Vinh Q.; Ali, Mona; Doniach, Sebastian; Jia, Tony Z.; Shan, Shu-ou

2010-01-01

Membrane proteins impose enormous challenges to cellular protein homeostasis during their post-translational targeting, and require chaperones to keep them soluble and translocation-competent. Here we show that a novel targeting factor in the chloroplast Signal Recognition Particle (cpSRP), cpSRP43, is a highly specific molecular chaperone that efficiently reverses the aggregation of its substrate proteins. In contrast to AAA+-chaperones, cpSRP43 utilizes specific binding interactions with its substrate to mediate its disaggregase activity. This ‘disaggregase’ capability can allow targeting machineries to more effectively capture their protein substrates, and emphasizes a close connection between protein folding and trafficking processes. Moreover, cpSRP43 provides the first example of an ATP-independent disaggregase, and demonstrates that efficient reversal of protein aggregation can be attained by specific binding interactions between a chaperone and its substrate. PMID:20424608

Activation of RIG-I-like Receptor Signal Transduction

PubMed Central

Bruns, Annie; Horvath, Curt M.

2011-01-01

Mammalian cells have the ability to recognize virus infection and mount a powerful antiviral response. Pattern recognition receptor proteins detect molecular signatures of virus infection and activate antiviral signaling cascades. The RIG-I-like receptors are cytoplasmic DExD/H box proteins that can specifically recognize virus-derived RNA species as a molecular feature discriminating the pathogen from the host. The RIG-I-like receptor family is composed of three homologous proteins, RIG-I, MDA5, and LGP2. All of these proteins can bind double-stranded RNA species with varying affinities via their conserved DExD/H box RNA helicase domains and C-terminal regulatory domains. The recognition of foreign RNA by the RLRs activates enzymatic functions and initiates signal transduction pathways resulting in the production of antiviral cytokines and the establishment of a broadly effective cellular antiviral state that protects neighboring cells from infection and triggers innate and adaptive immune systems. The propagation of this signal via the interferon antiviral system has been studied extensively, while the precise roles for enzymatic activities of the RNA helicase domain in antiviral responses are only beginning to be elucidated. Here, current models for RLR ligand recognition and signaling are reviewed. PMID:22066529

Comparing single- and dual-process models of memory development.

PubMed

Hayes, Brett K; Dunn, John C; Joubert, Amy; Taylor, Robert

2017-11-01

This experiment examined single-process and dual-process accounts of the development of visual recognition memory. The participants, 6-7-year-olds, 9-10-year-olds and adults, were presented with a list of pictures which they encoded under shallow or deep conditions. They then made recognition and confidence judgments about a list containing old and new items. We replicated the main trends reported by Ghetti and Angelini () in that recognition hit rates increased from 6 to 9 years of age, with larger age changes following deep than shallow encoding. Formal versions of the dual-process high threshold signal detection model and several single-process models (equal variance signal detection, unequal variance signal detection, mixture signal detection) were fit to the developmental data. The unequal variance and mixture signal detection models gave a better account of the data than either of the other models. A state-trace analysis found evidence for only one underlying memory process across the age range tested. These results suggest that single-process memory models based on memory strength are a viable alternative to dual-process models for explaining memory development. © 2016 John Wiley & Sons Ltd.

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

PubMed

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

Optical Pattern Recognition With Self-Amplification

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang

1994-01-01

In optical pattern recognition system with self-amplification, no reference beam used in addressing mode. Polarization of laser beam and orientation of photorefractive crystal chosen to maximize photorefractive effect. Intensity of recognition signal is orders of magnitude greater than other optical correlators. Apparatus regarded as real-time or quasi-real-time optical pattern recognizer with memory and reprogrammability.

Heme as a danger molecule in pathogen recognition.

PubMed

Wegiel, Barbara; Hauser, Carl J; Otterbein, Leo E

2015-12-01

Appropriate control of redox mechanisms are critical for and effective innate immune response, which employs multiple cell types, receptors and molecules that recognize danger signals when they reach the host. Recognition of pathogen-associated pattern molecules (PAMPs) is a fundamental host survival mechanism for efficient elimination of invading pathogens and resolution of the infection and inflammation. In addition to PAMPs, eukaryotic cells contain a plethora of intracellular molecules that are normally secured within the confines of the plasma membrane, but if liberated and encountered in the extracellular milieu can provoke rapid cell activation. These are known as Alarmins or Danger-Associated Molecular Patterns (DAMPs) and can be released actively by cells or passively as a result of sterile cellular injury after trauma, ischemia, or toxin-induced cell rupture. Both PAMPs and DAMPs are recognized by a series of cognate receptors that increase the generation of free radicals and activate specific signaling pathways that result in regulation of a variety of stress response, redox sensitive genes. Multiple mediators released, as cells die include, but are not limited to ATP, hydrogen peroxide, heme, formyl peptides, DNA or mitochondria provide the second signal to amplify immune responses. In this review, we will focus on how sterile and infective stimuli activate the stress response gene heme oxygenase-1 (Hmox1, HO-1), a master gene critical to an appropriate host response that is now recognized as one with enormous therapeutic potential. HO-1 gene expression is regulated in large part by redox-sensitive proteins including but not limited to nrf2. Both PAMPs and DAMPs increase the activation of nrf2 and HO-1. Heme is a powerful pro-oxidant and as such should be qualified as a DAMP. With its degradation by HO-1a molecule of carbon monoxide (CO) is generated that in turn serves as a bioactive signaling molecule. PAMPs such as bacterial endotoxin activate HO-1, and the CO that is generated diffuses into the extracellular milieu where it interacts with bacteria, altering their behavior to increase production of ATP, which then functions as a second signal danger molecule. This two-hit cycle scenario results in efficient and effective activation of host leukocytes to attack and clear bacteria in part via enhanced reactive oxygen species generation. We discuss this intimate communication that occurs between host and bacteria and how these molecules serve as critical regulators of the acute inflammatory response, the overall redox status of the cell, and survival of the host. Copyright © 2015 Elsevier Inc. All rights reserved.

The Membrane Transport System of the Guard Cell and Its Integration for Stomatal Dynamics1[CC-BY

PubMed Central

2017-01-01

Stomatal guard cells are widely recognized as the premier plant cell model for membrane transport, signaling, and homeostasis. This recognition is rooted in half a century of research into ion transport across the plasma and vacuolar membranes of guard cells that drive stomatal movements and the signaling mechanisms that regulate them. Stomatal guard cells surround pores in the epidermis of plant leaves, controlling the aperture of the pore to balance CO2 entry into the leaf for photosynthesis with water loss via transpiration. The position of guard cells in the epidermis is ideally suited for cellular and subcellular research, and their sensitivity to endogenous signals and environmental stimuli makes them a primary target for physiological studies. Stomata underpin the challenges of water availability and crop production that are expected to unfold over the next 20 to 30 years. A quantitative understanding of how ion transport is integrated and controlled is key to meeting these challenges and to engineering guard cells for improved water use efficiency and agricultural yields. PMID:28408539

The Membrane Transport System of the Guard Cell and Its Integration for Stomatal Dynamics.

PubMed

Jezek, Mareike; Blatt, Michael R

2017-06-01

Stomatal guard cells are widely recognized as the premier plant cell model for membrane transport, signaling, and homeostasis. This recognition is rooted in half a century of research into ion transport across the plasma and vacuolar membranes of guard cells that drive stomatal movements and the signaling mechanisms that regulate them. Stomatal guard cells surround pores in the epidermis of plant leaves, controlling the aperture of the pore to balance CO 2 entry into the leaf for photosynthesis with water loss via transpiration. The position of guard cells in the epidermis is ideally suited for cellular and subcellular research, and their sensitivity to endogenous signals and environmental stimuli makes them a primary target for physiological studies. Stomata underpin the challenges of water availability and crop production that are expected to unfold over the next 20 to 30 years. A quantitative understanding of how ion transport is integrated and controlled is key to meeting these challenges and to engineering guard cells for improved water use efficiency and agricultural yields. © 2017 The author(s). All Rights Reserved.

Person identification in irregular cardiac conditions using electrocardiogram signals.

PubMed

Sidek, Khairul Azami; Khalil, Ibrahim

2011-01-01

This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challenge database (AFPDB), MIT-BIH Supraventricular Arrthymia database (SVDB) and T-Wave Alternans Challenge database (TWADB). Cross correlation (CC) was used as the biometric matching algorithm with defined threshold values to evaluate the performance. In order to measure the efficiency of this simple yet effective matching algorithm, two biometric performance metrics were used which are false acceptance rate (FAR) and false reject rate (FRR). Our experimentation results suggest that ECG based biometric identification with irregular cardiac condition gives a higher recognition rate of different ECG signals when tested for three different abnormal cardiac databases yielding false acceptance rate (FAR) of 2%, 3% and 2% and false reject rate (FRR) of 1%, 2% and 0% for AFPDB, SVDB and TWADB respectively. These results also indicate the existence of salient biometric characteristics in the ECG morphology within the QRS complex that tends to differentiate individuals.

The regulatory mechanism of fungal elicitor-induced secondary metabolite biosynthesis in medical plants.

PubMed

Zhai, Xin; Jia, Min; Chen, Ling; Zheng, Cheng-Jian; Rahman, Khalid; Han, Ting; Qin, Lu-Ping

2017-03-01

A wide range of external stress stimuli trigger plant cells to undergo complex network of reactions that ultimately lead to the synthesis and accumulation of secondary metabolites. Accumulation of such metabolites often occurs in plants subjected to stresses including various elicitors or signal molecules. Throughout evolution, endophytic fungi, an important constituent in the environment of medicinal plants, have known to form long-term stable and mutually beneficial symbiosis with medicinal plants. The endophytic fungal elicitor can rapidly and specifically induce the expression of specific genes in medicinal plants which can result in the activation of a series of specific secondary metabolic pathways resulting in the significant accumulation of active ingredients. Here we summarize the progress made on the mechanisms of fungal elicitor including elicitor signal recognition, signal transduction, gene expression and activation of the key enzymes and its application. This review provides guidance on studies which may be conducted to promote the efficient synthesis and accumulation of active ingredients by the endogenous fungal elicitor in medicinal plant cells, and provides new ideas and methods of studying the regulation of secondary metabolism in medicinal plants.

Automated recognition system for power quality disturbances

NASA Astrophysics Data System (ADS)

Abdelgalil, Tarek

The application of deregulation policies in electric power systems has resulted in the necessity to quantify the quality of electric power. This fact highlights the need for a new monitoring strategy which is capable of tracking, detecting, classifying power quality disturbances, and then identifying the source of the disturbance. The objective of this work is to design an efficient and reliable power quality monitoring strategy that uses the advances in signal processing and pattern recognition to overcome the deficiencies that exist in power quality monitoring devices. The purposed monitoring strategy has two stages. The first stage is to detect, track, and classify any power quality violation by the use of on-line measurements. In the second stage, the source of the classified power quality disturbance must be identified. In the first stage, an adaptive linear combiner is used to detect power quality disturbances. Then, the Teager Energy Operator and Hilbert Transform are utilized for power quality event tracking. After the Fourier, Wavelet, and Walsh Transforms are employed for the feature extraction, two approaches are then exploited to classify the different power quality disturbances. The first approach depends on comparing the disturbance to be classified with a stored set of signatures for different power quality disturbances. The comparison is developed by using Hidden Markov Models and Dynamic Time Warping. The second approach depends on employing an inductive inference to generate the classification rules directly from the data. In the second stage of the new monitoring strategy, only the problem of identifying the location of the switched capacitor which initiates the transients is investigated. The Total Least Square-Estimation of Signal Parameters via Rotational Invariance Technique is adopted to estimate the amplitudes and frequencies of the various modes contained in the voltage signal measured at the facility entrance. After extracting the amplitudes and frequencies, an Artificial Neural Network is employed to identify the switched capacitor by using amplitudes and frequencies extracted from the transient signal. The new algorithms for detecting, tracking, and classifying power quality disturbances demonstrate the potential for further development of a fully automated recognition system for the assessment of power quality. This is possible because the implementation of the proposed algorithms for the power quality monitoring device becomes a straight forward process by modifying the device software.

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

PubMed

Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

2018-01-24

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Risperidone reverses the spatial object recognition impairment and hippocampal BDNF-TrkB signalling system alterations induced by acute MK-801 treatment

PubMed Central

Chen, Guangdong; Lin, Xiaodong; Li, Gongying; Jiang, Diego; Lib, Zhiruo; Jiang, Ronghuan; Zhuo, Chuanjun

2017-01-01

The aim of the present study was to investigate the effects of a commonly-used atypical antipsychotic, risperidone, on alterations in spatial learning and in the hippocampal brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signalling system caused by acute dizocilpine maleate (MK-801) treatment. In experiment 1, adult male Sprague-Dawley rats subjected to acute treatment of either low-dose MK801 (0.1 mg/kg) or normal saline (vehicle) were tested for spatial object recognition and hippocampal expression levels of BDNF, TrkB and the phophorylation of TrkB (p-TrkB). We found that compared to the vehicle, MK-801 treatment impaired spatial object recognition of animals and downregulated the expression levels of p-TrkB. In experiment 2, MK-801- or vehicle-treated animals were further injected with risperidone (0.1 mg/kg) or vehicle before behavioural testing and sacrifice. Of note, we found that risperidone successfully reversed the deleterious effects of MK-801 on spatial object recognition and upregulated the hippocampal BDNF-TrkB signalling system. Collectively, the findings suggest that cognitive deficits from acute N-methyl-D-aspartate receptor blockade may be associated with the hypofunction of hippocampal BDNF-TrkB signalling system and that risperidone was able to reverse these alterations. PMID:28451387

From cues to signals: evolution of interspecific communication via aposematism and mimicry in a predator-prey system.

PubMed

Lehmann, Kenna D S; Goldman, Brian W; Dworkin, Ian; Bryson, David M; Wagner, Aaron P

2014-01-01

Current theory suggests that many signaling systems evolved from preexisting cues. In aposematic systems, prey warning signals benefit both predator and prey. When the signal is highly beneficial, a third species often evolves to mimic the toxic species, exploiting the signaling system for its own protection. We investigated the evolutionary dynamics of predator cue utilization and prey signaling in a digital predator-prey system in which prey could evolve to alter their appearance to mimic poison-free or poisonous prey. In predators, we observed rapid evolution of cue recognition (i.e. active behavioral responses) when presented with sufficiently poisonous prey. In addition, active signaling (i.e. mimicry) evolved in prey under all conditions that led to cue utilization. Thus we show that despite imperfect and dishonest signaling, given a high cost of consuming poisonous prey, complex systems of interspecific communication can evolve via predator cue recognition and prey signal manipulation. This provides evidence supporting hypotheses that cues may serve as stepping-stones in the evolution of more advanced communication and signaling systems that incorporate information about the environment.

From Cues to Signals: Evolution of Interspecific Communication via Aposematism and Mimicry in a Predator-Prey System

PubMed Central

Lehmann, Kenna D. S.; Goldman, Brian W.; Dworkin, Ian; Bryson, David M.; Wagner, Aaron P.

2014-01-01

Current theory suggests that many signaling systems evolved from preexisting cues. In aposematic systems, prey warning signals benefit both predator and prey. When the signal is highly beneficial, a third species often evolves to mimic the toxic species, exploiting the signaling system for its own protection. We investigated the evolutionary dynamics of predator cue utilization and prey signaling in a digital predator-prey system in which prey could evolve to alter their appearance to mimic poison-free or poisonous prey. In predators, we observed rapid evolution of cue recognition (i.e. active behavioral responses) when presented with sufficiently poisonous prey. In addition, active signaling (i.e. mimicry) evolved in prey under all conditions that led to cue utilization. Thus we show that despite imperfect and dishonest signaling, given a high cost of consuming poisonous prey, complex systems of interspecific communication can evolve via predator cue recognition and prey signal manipulation. This provides evidence supporting hypotheses that cues may serve as stepping-stones in the evolution of more advanced communication and signaling systems that incorporate information about the environment. PMID:24614755

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

Reversing the picture superiority effect: a speed-accuracy trade-off study of recognition memory.

PubMed

Boldini, Angela; Russo, Riccardo; Punia, Sahiba; Avons, S E

2007-01-01

Speed-accuracy trade-off methods have been used to contrast single- and dual-process accounts of recognition memory. With these procedures, subjects are presented with individual test items and required to make recognition decisions under various time constraints. In three experiments, we presented words and pictures to be intentionally learned; test stimuli were always visually presented words. At test, we manipulated the interval between the presentation of each test stimulus and that of a response signal, thus controlling the amount of time available to retrieve target information. The standard picture superiority effect was significant in long response deadline conditions (i.e., > or = 2,000 msec). Conversely, a significant reverse picture superiority effect emerged at short response-signal deadlines (< 200 msec). The results are congruent with views suggesting that both fast familiarity and slower recollection processes contribute to recognition memory. Alternative accounts are also discussed.

Nonlinear changes in brain activity during continuous word repetition: an event-related multiparametric functional MR imaging study.

PubMed

Hagenbeek, R E; Rombouts, S A R B; Veltman, D J; Van Strien, J W; Witter, M P; Scheltens, P; Barkhof, F

2007-10-01

Changes in brain activation as a function of continuous multiparametric word recognition have not been studied before by using functional MR imaging (fMRI), to our knowledge. Our aim was to identify linear changes in brain activation and, what is more interesting, nonlinear changes in brain activation as a function of extended word repetition. Fifteen healthy young right-handed individuals participated in this study. An event-related extended continuous word-recognition task with 30 target words was used to study the parametric effect of word recognition on brain activation. Word-recognition-related brain activation was studied as a function of 9 word repetitions. fMRI data were analyzed with a general linear model with regressors for linearly changing signal intensity and nonlinearly changing signal intensity, according to group average reaction time (RT) and individual RTs. A network generally associated with episodic memory recognition showed either constant or linearly decreasing brain activation as a function of word repetition. Furthermore, both anterior and posterior cingulate cortices and the left middle frontal gyrus followed the nonlinear curve of the group RT, whereas the anterior cingulate cortex was also associated with individual RT. Linear alteration in brain activation as a function of word repetition explained most changes in blood oxygen level-dependent signal intensity. Using a hierarchically orthogonalized model, we found evidence for nonlinear activation associated with both group and individual RTs.

Association of impaired facial affect recognition with basic facial and visual processing deficits in schizophrenia.

PubMed

Norton, Daniel; McBain, Ryan; Holt, Daphne J; Ongur, Dost; Chen, Yue

2009-06-15

Impaired emotion recognition has been reported in schizophrenia, yet the nature of this impairment is not completely understood. Recognition of facial emotion depends on processing affective and nonaffective facial signals, as well as basic visual attributes. We examined whether and how poor facial emotion recognition in schizophrenia is related to basic visual processing and nonaffective face recognition. Schizophrenia patients (n = 32) and healthy control subjects (n = 29) performed emotion discrimination, identity discrimination, and visual contrast detection tasks, where the emotionality, distinctiveness of identity, or visual contrast was systematically manipulated. Subjects determined which of two presentations in a trial contained the target: the emotional face for emotion discrimination, a specific individual for identity discrimination, and a sinusoidal grating for contrast detection. Patients had significantly higher thresholds (worse performance) than control subjects for discriminating both fearful and happy faces. Furthermore, patients' poor performance in fear discrimination was predicted by performance in visual detection and face identity discrimination. Schizophrenia patients require greater emotional signal strength to discriminate fearful or happy face images from neutral ones. Deficient emotion recognition in schizophrenia does not appear to be determined solely by affective processing but is also linked to the processing of basic visual and facial information.

Mandarin Chinese Tone Identification in Cochlear Implants: Predictions from Acoustic Models

PubMed Central

Morton, Kenneth D.; Torrione, Peter A.; Throckmorton, Chandra S.; Collins, Leslie M.

2015-01-01

It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulation rate and current steering may provide the means of delivering more spectral information and thus may provide the auditory fine structure required for tone recognition. Several cochlear implant signal processing strategies are examined in this study, the continuous interleaved sampling (CIS) algorithm, the frequency amplitude modulation encoding (FAME) algorithm, and the multiple carrier frequency algorithm (MCFA). These strategies provide different types and amounts of spectral information. Pattern recognition techniques can be applied to data from Mandarin Chinese tone recognition tasks using acoustic models as a means of testing the abilities of these algorithms to transmit the changes in fundamental frequency indicative of the four lexical tones. The ability of processed Mandarin Chinese tones to be correctly classified may predict trends in the effectiveness of different signal processing algorithms in cochlear implants. The proposed techniques can predict trends in performance of the signal processing techniques in quiet conditions but fail to do so in noise. PMID:18706497

Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

PubMed

Ratan Murty, N Apurva; Arun, S P

2018-04-03

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively. Copyright © 2018 the Author(s). Published by PNAS.

A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors

PubMed Central

Cheng, Juan; Chen, Xun; Liu, Aiping; Peng, Hu

2015-01-01

Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing “Palm Downward” sign gestures from “Palm Inward” ones. Only the “Palm Inward” gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrats that sEMG signals are rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system. PMID:26389907

A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors.

PubMed

Cheng, Juan; Chen, Xun; Liu, Aiping; Peng, Hu

2015-09-15

Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing "Palm Downward" sign gestures from "Palm Inward" ones. Only the "Palm Inward" gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrats that sEMG signals are rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system.

Phylogenetic analysis and expression profiling of the pattern recognition receptors: Insights into molecular recognition of invading pathogens in Manduca sexta.

PubMed

Zhang, Xiufeng; He, Yan; Cao, Xiaolong; Gunaratna, Ramesh T; Chen, Yun-ru; Blissard, Gary; Kanost, Michael R; Jiang, Haobo

2015-07-01

Pattern recognition receptors (PRRs) detect microbial pathogens and trigger innate immune responses. Previous biochemical studies have elucidated the physiological functions of eleven PRRs in Manduca sexta but our understanding of the recognition process is still limited, lacking genomic perspectives. While 34 C-type lectin-domain proteins and 16 Toll-like receptors are reported in the companion papers, we present here 120 other putative PRRs identified through the genome annotation. These include 76 leucine-rich repeat (LRR) proteins, 14 peptidoglycan recognition proteins, 6 EGF/Nim-domain proteins, 5 β-1,3-glucanase-related proteins, 4 galectins, 4 fibrinogen-related proteins, 3 thioester proteins, 5 immunoglobulin-domain proteins, 2 hemocytins, and 1 Reeler. Sequence alignment and phylogenetic analysis reveal the evolution history of a diverse repertoire of proteins for pathogen recognition. While functions of insect LRR proteins are mostly unknown, their structure diversification is phenomenal: In addition to the Toll homologs, 22 LRR proteins with a signal peptide are expected to be secreted; 18 LRR proteins lacking signal peptides may be cytoplasmic; 36 LRRs with a signal peptide and a transmembrane segment may be non-Toll receptors on the surface of cells. Expression profiles of the 120 genes in 52 tissue samples reflect complex regulation in various developmental stages and physiological states, including some likely by Rel family transcription factors via κB motifs in the promoter regions. This collection of information is expected to facilitate future biochemical studies detailing their respective roles in this model insect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Phylogenetic analysis and expression profiling of the pattern recognition receptors: insights into molecular recognition of invading pathogens in Manduca sexta

PubMed Central

Zhang, Xiufeng; He, Yan; Cao, Xiaolong; Gunaratna, Ramesh T.; Chen, Yun-ru; Blissard, Gary; Kanost, Michael R.; Jiang, Haobo

2015-01-01

Pattern recognition receptors (PRRs) detect microbial pathogens and trigger innate immune responses. Previous biochemical studies have elucidated the physiological functions of eleven PRRs in Manduca sexta but our understanding of the recognition process is still limited, lacking genomic perspectives. While 34 C-type lectin-domain proteins and 16 Toll-like receptors are reported in the companion papers, we present here 120 other putative PRRs identified through the genome annotation. These include 76 leucine-rich repeat (LRR) proteins, 14 peptidoglycan recognition proteins, 6 EGF/Nim-domain proteins, 5 β-1,3-glucanase-related proteins, 4 galectins, 4 fibrinogen-related proteins, 3 thioester proteins, 5 immunoglobulin-domain proteins, 2 hemocytins, and 1 Reeler. Sequence alignment and phylogenetic analysis reveal the evolution history of a diverse repertoire of proteins for pathogen recognition. While functions of insect LRR proteins are mostly unknown, their structure diversification is phenomenal: In addition to the Toll homologs, 22 LRR proteins with a signal peptide are expected to be secreted; 18 LRR proteins lacking signal peptides may be cytoplasmic; 36 LRRs with a signal peptide and a transmembrane segment may be non-Toll receptors on the surface of cells. Expression profiles of the 120 genes in 52 tissue samples reflect complex regulation in various developmental stages and physiological states, including some likely by Rel family transcription factors via κB motifs in the promoter regions. This collection of information is expected to facilitate future biochemical studies detailing their respective roles in this model insect. PMID:25701384

Time-Frequency Analysis And Pattern Recognition Using Singular Value Decomposition Of The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Lovell, Brian; White, Langford

1988-01-01

Time-Frequency analysis based on the Wigner-Ville Distribution (WVD) is shown to be optimal for a class of signals where the variation of instantaneous frequency is the dominant characteristic. Spectral resolution and instantaneous frequency tracking is substantially improved by using a Modified WVD (MWVD) based on an Autoregressive spectral estimator. Enhanced signal-to-noise ratio may be achieved by using 2D windowing in the Time-Frequency domain. The WVD provides a tool for deriving descriptors of signals which highlight their FM characteristics. These descriptors may be used for pattern recognition and data clustering using the methods presented in this paper.

Medical application of artificial immune recognition system (AIRS): diagnosis of atherosclerosis from carotid artery Doppler signals.

PubMed

Latifoğlu, Fatma; Kodaz, Halife; Kara, Sadik; Güneş, Salih

2007-08-01

This study was conducted to distinguish between atherosclerosis and healthy subjects. Hence, we have employed the maximum envelope of the carotid artery Doppler sonograms derived from Fast Fourier Transformation-Welch method and Artificial Immune Recognition System (AIRS). The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and sometimes causes false diagnosis. Our technique gets around this problem using AIRS to decide and assist the physician to make the final judgment in confidence. AIRS has reached 99.29% classification accuracy using 10-fold cross validation. Results show that the proposed method classified Doppler signals successfully.

UV-radiation-induced electron emission by hormones. Hypothesis for specific communication mechanisms

NASA Astrophysics Data System (ADS)

Getoff, Nikola

2009-11-01

The highlights of recently observed electron emission from electronically excited sexual hormones (17β-estradiol, progesterone, testosterone) and the phytohormone genistein in polar media are briefly reviewed. The electron yield, Q(e aq-), dependence from substrate concentration, hormone structure, polarity of solvent, absorbed energy and temperature are discussed. The hormones reactivity with e aq- and efficiency in electron transfer ensure them the ability to communicate with other biological systems in an organism. A hypothesis is presented for the explanation of the mechanisms of the distinct recognition of signals transmitted by electrons, originating from different types of hormones to receiving centres. Biological consequences of the electron emission in respect to cancer are mentioned.

[Influence of music different in volume and style on human recognition activity].

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2009-01-01

The efficiency of recognition of masked visual images (Arabic numerals) under conditions of listening to classical (intensity 62 dB) or rock music (25 dB) increased. Coherence of potential in the frontal cortical region characteristic of the masked image recognition increased under conditions of listening to music. The changes in intercenter EEG relations were correlated with the formation of "the recognition dominant" at the behavioral level. Such behavioral and EEG changes were not observed during listening to louder music (85 dB) and listening to music of other styles, however, the coherence between potentials of the temporal and motor areas of the right hemisphere increased, and the latency of hand motor reactions decreased. The results suggest that the "recognition dominant" is formed under conditions of establishment of certain relations between the levels of excitation in the corresponding centers. These findings should be taken into consideration in case if it were necessary to increase the efficiency of the recognition.

Colocalization recognition-activated cascade signal amplification strategy for ultrasensitive detection of transcription factors.

PubMed

Zhu, Desong; Wang, Lei; Xu, Xiaowen; Jiang, Wei

2017-03-15

Transcription factors (TFs) bind to specific double-stranded DNA (dsDNA) sequences in the regulatory regions of genes to regulate the process of gene transcription. Their expression levels sensitively reflect cell developmental situation and disease state. TFs have become potential diagnostic markers and therapeutic targets of cancers and some other diseases. Hence, high sensitive detection of TFs is of vital importance for early diagnosis of diseases and drugs development. The traditional exonucleases-assisted signal amplification methods suffered from the false positives caused by incomplete digestion of excess recognition probes. Herein, based on a new recognition way-colocalization recognition (CR)-activated dual signal amplification, an ultrasensitive fluorescent detection strategy for TFs was developed. TFs-induced the colocalization of three split recognition components resulted in noticeable increases of local effective concentrations and hybridization of three split components, which activated the subsequent cascade signal amplification including strand displacement amplification (SDA) and exponential rolling circle amplification (ERCA). This strategy eliminated the false positive influence and achieved ultra-high sensitivity towards the purified NF-κB p50 with detection limit of 2.0×10 -13 M. Moreover, NF-κB p50 can be detected in as low as 0.21ngμL -1 HeLa cell nuclear extracts. In addition, this proposed strategy could be used for the screening of NF-κB p50 activity inhibitors and potential anti-NF-κB p50 drugs. Finally, our proposed strategy offered a potential method for reliable detection of TFs in medical diagnosis and treatment research of cancers and other related diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

Transmembrane insertion of twin-arginine signal peptides is driven by TatC and regulated by TatB

PubMed Central

Fröbel, Julia; Rose, Patrick; Lausberg, Frank; Blümmel, Anne-Sophie; Freudl, Roland; Müller, Matthias

2012-01-01

The twin-arginine translocation (Tat) pathway of bacteria and plant chloroplasts mediates the transmembrane transport of folded proteins, which harbour signal sequences with a conserved twin-arginine motif. Many Tat translocases comprise the three membrane proteins TatA, TatB and TatC. TatC was previously shown to be involved in recognizing twin-arginine signal peptides. Here we show that beyond recognition, TatC mediates the transmembrane insertion of a twin-arginine signal sequence, thereby translocating the signal sequence cleavage site across the bilayer. In the absence of TatB, this can lead to the removal of the signal sequence even from a translocation-incompetent substrate. Hence interaction of twin-arginine signal peptides with TatB counteracts their premature cleavage uncoupled from translocation. This capacity of TatB is not shared by the homologous TatA protein. Collectively our results suggest that TatC is an insertase for twin-arginine signal peptides and that translocation-proficient signal sequence recognition requires the concerted action of TatC and TatB. PMID:23250441

Transmembrane insertion of twin-arginine signal peptides is driven by TatC and regulated by TatB.

PubMed

Fröbel, Julia; Rose, Patrick; Lausberg, Frank; Blümmel, Anne-Sophie; Freudl, Roland; Müller, Matthias

2012-01-01

The twin-arginine translocation (Tat) pathway of bacteria and plant chloroplasts mediates the transmembrane transport of folded proteins, which harbour signal sequences with a conserved twin-arginine motif. Many Tat translocases comprise the three membrane proteins TatA, TatB and TatC. TatC was previously shown to be involved in recognizing twin-arginine signal peptides. Here we show that beyond recognition, TatC mediates the transmembrane insertion of a twin-arginine signal sequence, thereby translocating the signal sequence cleavage site across the bilayer. In the absence of TatB, this can lead to the removal of the signal sequence even from a translocation-incompetent substrate. Hence interaction of twin-arginine signal peptides with TatB counteracts their premature cleavage uncoupled from translocation. This capacity of TatB is not shared by the homologous TatA protein. Collectively our results suggest that TatC is an insertase for twin-arginine signal peptides and that translocation-proficient signal sequence recognition requires the concerted action of TatC and TatB.

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

NASA Astrophysics Data System (ADS)

Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong

2018-05-01

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

An Intelligent Pattern Recognition System Based on Neural Network and Wavelet Decomposition for Interpretation of Heart Sounds

DTIC Science & Technology

2001-10-25

wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a...Proceedings. pp. 415–418, 1990. [3] G. Ergun, “An intelligent diagnostic system for interpretation of arterpartum fetal heart rate tracings based on ANNs and...AN INTELLIGENT PATTERN RECOGNITION SYSTEM BASED ON NEURAL NETWORK AND WAVELET DECOMPOSITION FOR INTERPRETATION OF HEART SOUNDS I. TURKOGLU1, A

Introducing MINA--The Molecularly Imprinted Nanoparticle Assay.

PubMed

Shutov, Roman V; Guerreiro, Antonio; Moczko, Ewa; de Vargas-Sansalvador, Isabel Perez; Chianella, Iva; Whitcombe, Michael J; Piletsky, Sergey A

2014-03-26

A new ELISA- (enzyme-linked immunosorbent assay)-like assay is demonstrated in which no elements of biological origin are used for molecular recognition or signaling. Composite imprinted nanoparticles that contain a catalytic core and which are synthesized by using a solid-phase approach can simultaneously act as recognition/signaling elements, and be used with minimal modifications to standard assay protocols. This assay provides a new route towards replacement of unstable biomolecules in immunoassays. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Surface EMG signals based motion intent recognition using multi-layer ELM

NASA Astrophysics Data System (ADS)

Wang, Jianhui; Qi, Lin; Wang, Xiao

2017-11-01

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

Implementation of a Peltier-based cooling device for localized deep cortical deactivation during in vivo object recognition testing

NASA Astrophysics Data System (ADS)

Marra, Kyle; Graham, Brett; Carouso, Samantha; Cox, David

2012-02-01

While the application of local cortical cooling has recently become a focus of neurological research, extended localized deactivation deep within brain structures is still unexplored. Using a wirelessly controlled thermoelectric (Peltier) device and water-based heat sink, we have achieved inactivating temperatures (<20 C) at greater depths (>8 mm) than previously reported. After implanting the device into Long Evans rats' basolateral amygdala (BLA), an inhibitory brain center that controls anxiety and fear, we ran an open field test during which anxiety-driven behavioral tendencies were observed to decrease during cooling, thus confirming the device's effect on behavior. Our device will next be implanted in the rats' temporal association cortex (TeA) and recordings from our signal-tracing multichannel microelectrodes will measure and compare activated and deactivated neuronal activity so as to isolate and study the TeA signals responsible for object recognition. Having already achieved a top performing computational face-recognition system, the lab will utilize this TeA activity data to generalize its computational efforts of face recognition to achieve general object recognition.

Molecular Pathways for Immune Recognition of Preproinsulin Signal Peptide in Type 1 Diabetes.

PubMed

Kronenberg-Versteeg, Deborah; Eichmann, Martin; Russell, Mark A; de Ru, Arnoud; Hehn, Beate; Yusuf, Norkhairin; van Veelen, Peter A; Richardson, Sarah J; Morgan, Noel G; Lemberg, Marius K; Peakman, Mark

2018-04-01

The signal peptide region of preproinsulin (PPI) contains epitopes targeted by HLA-A-restricted (HLA-A0201, A2402) cytotoxic T cells as part of the pathogenesis of β-cell destruction in type 1 diabetes. We extended the discovery of the PPI epitope to disease-associated HLA-B*1801 and HLA-B*3906 (risk) and HLA-A*1101 and HLA-B*3801 (protective) alleles, revealing that four of six alleles present epitopes derived from the signal peptide region. During cotranslational translocation of PPI, its signal peptide is cleaved and retained within the endoplasmic reticulum (ER) membrane, implying it is processed for immune recognition outside of the canonical proteasome-directed pathway. Using in vitro translocation assays with specific inhibitors and gene knockout in PPI-expressing target cells, we show that PPI signal peptide antigen processing requires signal peptide peptidase (SPP). The intramembrane protease SPP generates cytoplasm-proximal epitopes, which are transporter associated with antigen processing (TAP), ER-luminal epitopes, which are TAP independent, each presented by different HLA class I molecules and N-terminal trimmed by ER aminopeptidase 1 for optimal presentation. In vivo, TAP expression is significantly upregulated and correlated with HLA class I hyperexpression in insulin-containing islets of patients with type 1 diabetes. Thus, PPI signal peptide epitopes are processed by SPP and loaded for HLA-guided immune recognition via pathways that are enhanced during disease pathogenesis. © 2018 by the American Diabetes Association.

Computer Vision for Artificially Intelligent Robotic Systems

NASA Astrophysics Data System (ADS)

Ma, Chialo; Ma, Yung-Lung

1987-04-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts -- position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed bye the main control unit. In Pulse-Echo Signal Process Unit, we ultilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by u law coding method, and this data together with delay time T, angle information OH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Model, we use a narrow beam transducer and it's input voltage is 50V p-p. A RobOt equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Improved Performance Characteristics For Indium Antimonide Photovoltaic Detector Arrays Using A FET-Switched Multiplexing Technique

NASA Astrophysics Data System (ADS)

Ma, Yung-Lung; Ma, Chialo

1987-03-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts _ position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed by the main control unit. In Pulse-Echo Signal Process Unit, we utilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by p law coding method, and this data together with delay time T, angle information eH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Models, we use a narrow beam transducer and it's input voltage is 50V p-p. A Robot equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Analysis of physiological signals for recognition of boredom, pain, and surprise emotions.

PubMed

Jang, Eun-Hye; Park, Byoung-Jun; Park, Mi-Sook; Kim, Sang-Hyeob; Sohn, Jin-Hun

2015-06-18

The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals. Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions. The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7% is obtained by using DFA. This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.

Breaking cover: neural responses to slow and fast camouflage-breaking motion.

PubMed

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M; McLoughlin, Niall; Wang, Wei

2015-08-22

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. © 2015 The Authors.

Breaking cover: neural responses to slow and fast camouflage-breaking motion

PubMed Central

Yin, Jiapeng; Gong, Hongliang; An, Xu; Chen, Zheyuan; Lu, Yiliang; Andolina, Ian M.; McLoughlin, Niall; Wang, Wei

2015-01-01

Primates need to detect and recognize camouflaged animals in natural environments. Camouflage-breaking movements are often the only visual cue available to accomplish this. Specifically, sudden movements are often detected before full recognition of the camouflaged animal is made, suggesting that initial processing of motion precedes the recognition of motion-defined contours or shapes. What are the neuronal mechanisms underlying this initial processing of camouflaged motion in the primate visual brain? We investigated this question using intrinsic-signal optical imaging of macaque V1, V2 and V4, along with computer simulations of the neural population responses. We found that camouflaged motion at low speed was processed as a direction signal by both direction- and orientation-selective neurons, whereas at high-speed camouflaged motion was encoded as a motion-streak signal primarily by orientation-selective neurons. No population responses were found to be invariant to the camouflage contours. These results suggest that the initial processing of camouflaged motion at low and high speeds is encoded as direction and motion-streak signals in primate early visual cortices. These processes are consistent with a spatio-temporal filter mechanism that provides for fast processing of motion signals, prior to full recognition of camouflage-breaking animals. PMID:26269500

Flight calls signal group and individual identity but not kinship in a cooperatively breeding bird.

PubMed

Keen, Sara C; Meliza, C Daniel; Rubenstein, Dustin R

2013-11-01

In many complex societies, intricate communication and recognition systems may evolve to help support both direct and indirect benefits of group membership. In cooperatively breeding species where groups typically comprise relatives, both learned and innate vocal signals may serve as reliable cues for kin recognition. Here, we investigated vocal communication in the plural cooperatively breeding superb starling, Lamprotornis superbus , where flight calls-short, stereotyped vocalizations used when approaching conspecifics-may communicate kin relationships, group membership, and/or individual identity. We found that flight calls were most similar within individual repertoires but were also more similar within groups than within the larger population. Although starlings responded differently to playback of calls from their own versus other neighboring and distant social groups, call similarity was uncorrelated with genetic relatedness. Additionally, immigrant females showed similar patterns to birds born in the study population. Together, these results suggest that flight calls are learned signals that reflect social association but may also carry a signal of individuality. Flight calls, therefore, provide a reliable recognition mechanism for groups and may also be used to recognize individuals. In complex societies comprising related and unrelated individuals, signaling individuality and group association, rather than kinship, may be a route to cooperation.

Drastic changes in conformational dynamics of the antiterminator M2-1 regulate transcription efficiency in Pneumovirinae

PubMed Central

Leyrat, Cedric; Renner, Max; Harlos, Karl; Huiskonen, Juha T; Grimes, Jonathan M

2014-01-01

The M2-1 protein of human metapneumovirus (HMPV) is a zinc-binding transcription antiterminator which is highly conserved among pneumoviruses. We report the structure of tetrameric HMPV M2-1. Each protomer features a N-terminal zinc finger domain and an α-helical tetramerization motif forming a rigid unit, followed by a flexible linker and an α-helical core domain. The tetramer is asymmetric, three of the protomers exhibiting a closed conformation, and one an open conformation. Molecular dynamics simulations and SAXS demonstrate a dynamic equilibrium between open and closed conformations in solution. Structures of adenosine monophosphate- and DNA- bound M2-1 establish the role of the zinc finger domain in base-specific recognition of RNA. Binding to ‘gene end’ RNA sequences stabilized the closed conformation of M2-1 leading to a drastic shift in the conformational landscape of M2-1. We propose a model for recognition of gene end signals and discuss the implications of these findings for transcriptional regulation in pneumoviruses. DOI: http://dx.doi.org/10.7554/eLife.02674.001 PMID:24842877

Distributed Fusion in Sensor Networks with Information Genealogy

DTIC Science & Technology

2011-06-28

image processing [2], acoustic and speech recognition [3], multitarget tracking [4], distributed fusion [5], and Bayesian inference [6-7]. For...Adaptation for Distant-Talking Speech Recognition." in Proc Acoustics. Speech , and Signal Processing, 2004 |4| Y Bar-Shalom and T 1-. Fortmann...used in speech recognition and other classification applications [8]. But their use in underwater mine classification is limited. In this paper, we

Influence of music with different volumes and styles on recognition activity in humans.

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2010-10-01

The efficiency of the recognition of masked visual images (Arabic numerals) increased when accompanied by classical (62 dB) and rock music (25 dB). These changes were accompanied by increases in the coherence of potentials in the frontal areas seen on recognition without music. Changes in intercenter EEG relationships correlated with the formation a dominant at the behavioral level. When loud music (85 dB) and music of other styles was used, these changes in behavior and the EEG were not seen; however, the coherence of potentials in the temporal and motor cortex of the right hemisphere increased and the latent periods of motor reactions of the hands decreased. These results provide evidence that the "recognition" dominant is formed when there are particular ratios of the levels of excitation in the corresponding centers, which should be considered when there is a need to increase the efficiency of recognition activity in humans.

A novel "signal-on/off" sensing platform for selective detection of thrombin based on target-induced ratiometric electrochemical biosensing and bio-bar-coded nanoprobe amplification strategy.

PubMed

Wang, Lanlan; Ma, Rongna; Jiang, Liushan; Jia, Liping; Jia, Wenli; Wang, Huaisheng

2017-06-15

A novel dual-signal ratiometric electrochemical aptasensor for highly sensitive and selective detection of thrombin has been designed on the basis of signal-on and signal-off strategy. Ferrocene labeled hairpin probe (Fc-HP), thrombin aptamer and methyl blue labeled bio-bar-coded AuNPs (MB-P3-AuNPs) were rationally introduced for the construction of the assay platform, which combined the advantages of the recognition of aptamer, the amplification of bio-bar-coded nanoprobe, and the ratiometric signaling readout. In the presence of thrombin, the interaction between thrombin and the aptamer leads to the departure of MB-P3-AuNPs from the sensing interface, and the conformation of the single stranded Fc-HP to a hairpin structure to take the Fc confined near the electrode surface. Such conformational changes resulted in the oxidation current of Fc increased and that of MB decreased. Therefore, the recognition event of the target can be dual-signal ratiometric electrochemical readout in both the "signal-off" of MB and the "signal-on" of Fc. The proposed strategy showed a wide linear detection range from 0.003 to 30nM with a detection limit of 1.1 pM. Moreover, it exhibits good performance of excellent selectivity, good stability, and acceptable fabrication reproducibility. By changing the recognition probe, this protocol could be easily expanded into the detection of other targets, showing promising potential applications in disease diagnostics and bioanalysis. Copyright © 2016. Published by Elsevier B.V.

The Calcineurin Signaling Network Evolves Via Conserved Kinase–Phosphatase Modules That Transcend Substrate Identity

PubMed Central

Bodenmiller, Bernd; Wanka, Stefanie; Landry, Christian R.; Aebersold, Ruedi; Cyert, Martha S.

2014-01-01

Summary To define the first functional network for calcineurin, the conserved Ca2+/calmodulin-regulated phosphatase, we systematically identified its substrates in S. cerevisiae using phosphoproteomics and bioinformatics, followed by co-purification and dephosphorylation assays. This study establishes new calcineurin functions and reveals mechanisms that shape calcineurin network evolution. Analyses of closely related yeasts show that many proteins were recently recruited to the network by acquiring a calcineurin-recognition motif. Calcineurin substrates in yeast and mammals are distinct due to network rewiring but surprisingly are phosphorylated by similar kinases. We postulate that co-recognition of conserved substrate features, including phosphorylation and docking motifs, preserves calcineurin-kinase opposition during evolution. One example we document is a composite docking site that confers substrate recognition by both calcineurin and MAPK. We propose that conserved kinase-phosphatase pairs define the architecture of signaling networks and allow other connections between kinases and phosphatases to develop and establish common regulatory motifs in signaling networks. PMID:24930733

Looking for myself: current multisensory input alters self-face recognition.

PubMed

Tsakiris, Manos

2008-01-01

How do I know the person I see in the mirror is really me? Is it because I know the person simply looks like me, or is it because the mirror reflection moves when I move, and I see it being touched when I feel touch myself? Studies of face-recognition suggest that visual recognition of stored visual features inform self-face recognition. In contrast, body-recognition studies conclude that multisensory integration is the main cue to selfhood. The present study investigates for the first time the specific contribution of current multisensory input for self-face recognition. Participants were stroked on their face while they were looking at a morphed face being touched in synchrony or asynchrony. Before and after the visuo-tactile stimulation participants performed a self-recognition task. The results show that multisensory signals have a significant effect on self-face recognition. Synchronous tactile stimulation while watching another person's face being similarly touched produced a bias in recognizing one's own face, in the direction of the other person included in the representation of one's own face. Multisensory integration can update cognitive representations of one's body, such as the sense of ownership. The present study extends this converging evidence by showing that the correlation of synchronous multisensory signals also updates the representation of one's face. The face is a key feature of our identity, but at the same time is a source of rich multisensory experiences used to maintain or update self-representations.

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

PubMed

Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

2017-07-12

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

The Effect of Lexical Content on Dichotic Speech Recognition in Older Adults.

PubMed

Findlen, Ursula M; Roup, Christina M

2016-01-01

Age-related auditory processing deficits have been shown to negatively affect speech recognition for older adult listeners. In contrast, older adults gain benefit from their ability to make use of semantic and lexical content of the speech signal (i.e., top-down processing), particularly in complex listening situations. Assessment of auditory processing abilities among aging adults should take into consideration semantic and lexical content of the speech signal. The purpose of this study was to examine the effects of lexical and attentional factors on dichotic speech recognition performance characteristics for older adult listeners. A repeated measures design was used to examine differences in dichotic word recognition as a function of lexical and attentional factors. Thirty-five older adults (61-85 yr) with sensorineural hearing loss participated in this study. Dichotic speech recognition was evaluated using consonant-vowel-consonant (CVC) word and nonsense CVC syllable stimuli administered in the free recall, directed recall right, and directed recall left response conditions. Dichotic speech recognition performance for nonsense CVC syllables was significantly poorer than performance for CVC words. Dichotic recognition performance varied across response condition for both stimulus types, which is consistent with previous studies on dichotic speech recognition. Inspection of individual results revealed that five listeners demonstrated an auditory-based left ear deficit for one or both stimulus types. Lexical content of stimulus materials affects performance characteristics for dichotic speech recognition tasks in the older adult population. The use of nonsense CVC syllable material may provide a way to assess dichotic speech recognition performance while potentially lessening the effects of lexical content on performance (i.e., measuring bottom-up auditory function both with and without top-down processing). American Academy of Audiology.

Speech Clarity Index (Ψ): A Distance-Based Speech Quality Indicator and Recognition Rate Prediction for Dysarthric Speakers with Cerebral Palsy

NASA Astrophysics Data System (ADS)

Kayasith, Prakasith; Theeramunkong, Thanaruk

It is a tedious and subjective task to measure severity of a dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (Ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of Ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that Ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.

A facile approach to construct versatile signal amplification system for bacterial detection.

PubMed

Qi, Peng; Zhang, Dun; Wan, Yi; Lv, Dandan

2014-01-01

In this work, a facile approach to design versatile signal amplification system for bacterial detection has been presented. Bio-recognition elements and signaling molecules can be immobilized on the surface of Fe₃O₄@MnO₂ nanomaterials with the help of bioinspired polydopamine (PDA). Fe₃O₄@MnO₂ nanoplates were chosen as carrier for bio-recognizing and signaling molecules because this kind of nanomaterial was superparamagnetic and the existence of MnO₂ could enhance the polymerization of dopamine due to its strong oxidative ability. This nanocomposite system was versatile because PDA around Fe₃O₄@MnO₂ nanoplates provided a stable and convenient platform for immobilization of biological and chemical materials, and various kinds of bio-recognizing and signaling molecules could be immobilized by reaction with pendant amino groups of dopamine to meet different detection requirements. Since a substantial amount of signaling molecules were immobilized on the surface of the nanocomposites, so the sensitivity of detection would be improved when the prepared nanocomposites were selectively conjugated with target pathogen. In the experimental section, a sandwich-type electrochemical biosensor was developed to verify the amplified bacterial detection sensitivity. Concanavalin A (conA) and ferrocene (Fc) were chosen as bio-recognition elements and signaling molecules for detection of Desulforibrio caledoiensis, respectively. The conA and Fc modified nanocomposites were conjugated on electrode by the selective recognition between conA and target bacteria, and the bacterial population was obtained by quantification of the electrochemical signal of Fc moieties. The experimental results showed that the detection sensitivity for D. caledoiensis was improved by taking advantage of this signal amplification system. © 2013 Elsevier B.V. All rights reserved.

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology

PubMed Central

Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

2017-01-01

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space (H, L, and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved. PMID:28106806

UHF Signal Processing and Pattern Recognition of Partial Discharge in Gas-Insulated Switchgear Using Chromatic Methodology.

PubMed

Wang, Xiaohua; Li, Xi; Rong, Mingzhe; Xie, Dingli; Ding, Dan; Wang, Zhixiang

2017-01-18

The ultra-high frequency (UHF) method is widely used in insulation condition assessment. However, UHF signal processing algorithms are complicated and the size of the result is large, which hinders extracting features and recognizing partial discharge (PD) patterns. This article investigated the chromatic methodology that is novel in PD detection. The principle of chromatic methodologies in color science are introduced. The chromatic processing represents UHF signals sparsely. The UHF signals obtained from PD experiments were processed using chromatic methodology and characterized by three parameters in chromatic space ( H , L , and S representing dominant wavelength, signal strength, and saturation, respectively). The features of the UHF signals were studied hierarchically. The results showed that the chromatic parameters were consistent with conventional frequency domain parameters. The global chromatic parameters can be used to distinguish UHF signals acquired by different sensors, and they reveal the propagation properties of the UHF signal in the L-shaped gas-insulated switchgear (GIS). Finally, typical PD defect patterns had been recognized by using novel chromatic parameters in an actual GIS tank and good performance of recognition was achieved.

Voice Recognition: A New Assessment Tool?

ERIC Educational Resources Information Center

Jones, Darla

2005-01-01

This article presents the results of a study conducted in Anchorage, Alaska, that evaluated the accuracy and efficiency of using voice recognition (VR) technology to collect oral reading fluency data for classroom-based assessments. The primary research question was as follows: Is voice recognition technology a valid and reliable alternative to…

Utilizing a State Level Volunteer Recognition Program at the County Level

ERIC Educational Resources Information Center

McCall, Fran Korthaus; Culp, Ken, III

2013-01-01

Volunteer recognition is an important component of Extension programs. Most land-grant universities have implemented a state volunteer recognition program. Extension professionals, however, are too overburdened with meetings, programs, and activities to effectively recognize volunteers locally. Utilizing a state model is an efficient means of…

Development of Encoding and Decision Processes in Visual Recognition.

ERIC Educational Resources Information Center

Newcombe, Nora; MacKenzie, Doris L.

This experiment examined two processes which might account for developmental increases in accuracy in visual recognition tasks: age-related increases in efficiency of scanning during inspection, and age-related increases in the ability to make decisions systematically during test. Critical details necessary for recognition were highlighted as…

Adult Word Recognition and Visual Sequential Memory

ERIC Educational Resources Information Center

Holmes, V. M.

2012-01-01

Two experiments were conducted investigating the role of visual sequential memory skill in the word recognition efficiency of undergraduate university students. Word recognition was assessed in a lexical decision task using regularly and strangely spelt words, and nonwords that were either standard orthographically legal strings or items made from…

Implementation study of wearable sensors for activity recognition systems.

PubMed

Rezaie, Hamed; Ghassemian, Mona

2015-08-01

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely 'stream-based', 'feature-based' and 'threshold-based' scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency.

Chimeric antigen receptor containing ICOS signaling domain mediates specific and efficient antitumor effect of T cells against EGFRvIII expressing glioma

PubMed Central

2013-01-01

Background Adoptive transfer of chimeric antigen receptor (CAR)-modified T cells appears to be a promising immunotherapeutic strategy. CAR combines the specificity of antibody and cytotoxicity of cytotoxic T lymphocytes, enhancing T cells’ ability to specifically target antigens and to effectively kill cancer cells. Recent efforts have been made to integrate the costimulatory signals in the CAR to improve the antitumor efficacy. Epidermal growth factor receptor variant III (EGFRvIII) is an attractive therapeutic target as it frequently expresses in glioma and many other types of cancers. Our current study aimed to investigate the specific and efficient antitumor effect of T cells modified with CAR containing inducible costimulator (ICOS) signaling domain. Methods A second generation of EGFRvIII/CAR was generated and it contained the EGFRvIII single chain variable fragment, ICOS signaling domain and CD3ζ chain. Lentiviral EGFRvIII/CAR was prepared and human CD3+ T cells were infected by lentivirus encoding EGFRvIII/CAR. The expression of EGFRvIII/CAR on CD3+ T cells was confirmed by flow cytometry and Western blot. The functions of EGFRvIII/CAR+ T cells were evaluated using in vitro and in vivo methods including cytotoxicity assay, cytokine release assay and xenograft tumor mouse model. Results Chimeric EGFRvIIIscFv-ICOS-CD3ζ (EGFRvIII/CAR) was constructed and lentiviral EGFRvIII/CAR were made to titer of 106 TU/ml. The transduction efficiency of lentiviral EGFRvIII/CAR on T cells reached around 70% and expression of EGFRvIII/CAR protein was verified by immunoblotting as a band of about 57 kDa. Four hour 51Cr release assays demonstrated specific and efficient cytotoxicity of EGFRvIII/CAR+ T cells against EGFRvIII expressing U87 cells. A robust increase in the IFN-γ secretion was detected in the co-culture supernatant of the EGFRvIII/CAR+ T cells and the EGFRvIII expressing U87 cells. Intravenous and intratumor injection of EGFRvIII/CAR+ T cells inhibited the in vivo growth of the EGFRvIII expressing glioma cells. Conclusions Our study demonstrates that the EGFRvIII/CAR-modified T cells can destroy glioma cells efficiently in an EGFRvIII specific manner and release IFN-γ in an antigen dependent manner. The specific recognition and effective killing activity of the EGFRvIII-directed T cells with ICOS signaling domain lays a foundation for us to employ such approach in future cancer treatment. PMID:23656794

Chimeric antigen receptor containing ICOS signaling domain mediates specific and efficient antitumor effect of T cells against EGFRvIII expressing glioma.

PubMed

Shen, Chan-Juan; Yang, Yu-Xiu; Han, Ethan Q; Cao, Na; Wang, Yun-Fei; Wang, Yi; Zhao, Ying-Ying; Zhao, Li-Ming; Cui, Jian; Gupta, Puja; Wong, Albert J; Han, Shuang-Yin

2013-05-09

Adoptive transfer of chimeric antigen receptor (CAR)-modified T cells appears to be a promising immunotherapeutic strategy. CAR combines the specificity of antibody and cytotoxicity of cytotoxic T lymphocytes, enhancing T cells' ability to specifically target antigens and to effectively kill cancer cells. Recent efforts have been made to integrate the costimulatory signals in the CAR to improve the antitumor efficacy. Epidermal growth factor receptor variant III (EGFRvIII) is an attractive therapeutic target as it frequently expresses in glioma and many other types of cancers. Our current study aimed to investigate the specific and efficient antitumor effect of T cells modified with CAR containing inducible costimulator (ICOS) signaling domain. A second generation of EGFRvIII/CAR was generated and it contained the EGFRvIII single chain variable fragment, ICOS signaling domain and CD3ζ chain. Lentiviral EGFRvIII/CAR was prepared and human CD3+ T cells were infected by lentivirus encoding EGFRvIII/CAR. The expression of EGFRvIII/CAR on CD3+ T cells was confirmed by flow cytometry and Western blot. The functions of EGFRvIII/CAR+ T cells were evaluated using in vitro and in vivo methods including cytotoxicity assay, cytokine release assay and xenograft tumor mouse model. Chimeric EGFRvIIIscFv-ICOS-CD3ζ (EGFRvIII/CAR) was constructed and lentiviral EGFRvIII/CAR were made to titer of 106 TU/ml. The transduction efficiency of lentiviral EGFRvIII/CAR on T cells reached around 70% and expression of EGFRvIII/CAR protein was verified by immunoblotting as a band of about 57 kDa. Four hour 51Cr release assays demonstrated specific and efficient cytotoxicity of EGFRvIII/CAR+ T cells against EGFRvIII expressing U87 cells. A robust increase in the IFN-γ secretion was detected in the co-culture supernatant of the EGFRvIII/CAR+ T cells and the EGFRvIII expressing U87 cells. Intravenous and intratumor injection of EGFRvIII/CAR+ T cells inhibited the in vivo growth of the EGFRvIII expressing glioma cells. Our study demonstrates that the EGFRvIII/CAR-modified T cells can destroy glioma cells efficiently in an EGFRvIII specific manner and release IFN-γ in an antigen dependent manner. The specific recognition and effective killing activity of the EGFRvIII-directed T cells with ICOS signaling domain lays a foundation for us to employ such approach in future cancer treatment.

Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals.

PubMed

Grützmacher, Florian; Beichler, Benjamin; Hein, Albert; Kirste, Thomas; Haubelt, Christian

2018-05-23

Piecewise linear approximation of sensor signals is a well-known technique in the fields of Data Mining and Activity Recognition. In this context, several algorithms have been developed, some of them with the purpose to be performed on resource constrained microcontroller architectures of wireless sensor nodes. While microcontrollers are usually constrained in computational power and memory resources, all state-of-the-art piecewise linear approximation techniques either need to buffer sensor data or have an execution time depending on the segment’s length. In the paper at hand, we propose a novel piecewise linear approximation algorithm, with a constant computational complexity as well as a constant memory complexity. Our proposed algorithm’s worst-case execution time is one to three orders of magnitude smaller and its average execution time is three to seventy times smaller compared to the state-of-the-art Piecewise Linear Approximation (PLA) algorithms in our experiments. In our evaluations, we show that our algorithm is time and memory efficient without sacrificing the approximation quality compared to other state-of-the-art piecewise linear approximation techniques, while providing a maximum error guarantee per segment, a small parameter space of only one parameter, and a maximum latency of one sample period plus its worst-case execution time.

M13 procoat protein insertion into YidC and SecYEG proteoliposomes and liposomes.

PubMed

Stiegler, Natalie; Dalbey, Ross E; Kuhn, Andreas

2011-02-25

M13 procoat protein was one of the first model proteins used to study bacterial membrane protein insertion. It contains a signal peptide of 23 amino acid residues and is not membrane targeted by the signal recognition particle. The translocation of its periplasmic domain is independent of the preprotein translocase (SecAYEG) but requires electrochemical membrane potential and the membrane insertase YidC of Escherichia coli. We show here that YidC is sufficient for efficient membrane insertion of the purified M13 procoat protein into energized YidC proteoliposomes. When no membrane potential is applied, the insertion is substantially reduced. Only in the presence of YidC, membrane insertion occurs if bilayer integrity is preserved and membrane potential is stable for more than 20 min. A mutant of the M13 procoat protein, H5EE, with two additional negatively charged residues in the periplasmic domain inserted into YidC proteoliposomes and SecYEG proteoliposomes with equal efficiencies. We conclude that the protein can use both the YidC-only pathway and the Sec pathway. This poses the questions of how procoat H5EE is inserted in vivo and how insertion pathways are selected in the cell. Copyright © 2011 Elsevier Ltd. All rights reserved.

Urtica dioica modulates hippocampal insulin signaling and recognition memory deficit in streptozotocin induced diabetic mice.

PubMed

Patel, Sita Sharan; Gupta, Sahil; Udayabanu, Malairaman

2016-06-01

Diabetes mellitus has been associated with functional abnormalities in the hippocampus and performance of cognitive function. Urtica dioica (UD) has been used in the treatment of diabetes. In our previous report we observed that UD extract attenuate diabetes mediated associative and spatial memory dysfunction. The present study aimed to evaluate the effect of UD extract on mouse model of diabetes-induced recognition memory deficit and explore the possible mechanism behind it. Streptozotocin (STZ) (50 mg/kg, i.p. consecutively for 5 days) was used to induce diabetes followed by UD extract (50 mg/kg, oral) or rosiglitazone (ROSI) (5 mg/kg, oral) administration for 8 weeks. STZ induced diabetic mice showed significant decrease in hippocampal insulin signaling and translocation of glucose transporter type 4 (GLUT4) to neuronal membrane resulting in cognitive dysfunction and hypolocomotion. UD treatment effectively improved hippocampal insulin signaling, glucose tolerance and recognition memory performance in diabetic mice, which was comparable to ROSI. Further, diabetes mediated oxidative stress and inflammation was reversed by chronic UD or ROSI administration. UD leaves extract acts via insulin signaling pathway and might prove to be effective for the diabetes mediated central nervous system complications.

A continuous dual-process model of remember/know judgments.

PubMed

Wixted, John T; Mickes, Laura

2010-10-01

The dual-process theory of recognition memory holds that recognition decisions can be based on recollection or familiarity, and the remember/know procedure is widely used to investigate those 2 processes. Dual-process theory in general and the remember/know procedure in particular have been challenged by an alternative strength-based interpretation based on signal-detection theory, which holds that remember judgments simply reflect stronger memories than do know judgments. Although supported by a considerable body of research, the signal-detection account is difficult to reconcile with G. Mandler's (1980) classic "butcher-on-the-bus" phenomenon (i.e., strong, familiarity-based recognition). In this article, a new signal-detection model is proposed that does not deny either the validity of dual-process theory or the possibility that remember/know judgments can-when used in the right way-help to distinguish between memories that are largely recollection based from those that are largely familiarity based. It does, however, agree with all prior signal-detection-based critiques of the remember/know procedure, which hold that, as it is ordinarily used, the procedure mainly distinguishes strong memories from weak memories (not recollection from familiarity).

Real-time mental arithmetic task recognition from EEG signals.

PubMed

Wang, Qiang; Sourina, Olga

2013-03-01

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

Pinniped bioacoustics: Atmospheric and hydrospheric signal production, reception, and function

NASA Astrophysics Data System (ADS)

Schusterman, Ronald J.; Kastak, David; Reichmuth Kastak, Colleen; Holt, Marla; Southall, Brandon L.

2004-05-01

There is no convincing evidence that any of the 33 pinniped species evolved acoustic specializations for echolocation. However, all species produce and localize signals amphibiously in different communicative contexts. In the setting of sexual selection, aquatic mating male phocids and walruses tend to emit underwater calls, while male otariids and phocids that breed terrestrially emit airborne calls. Signature vocalizations are widespread among pinnipeds. There is evidence that males use signature threat calls, and it is possible that vocal recognition may be used by territorial males to form categories consisting of neighbors and strangers. In terms of mother-offspring recognition, both otariid females and their pups use acoustical cues for mutual recognition. In contrast, reunions between phocid females and their dependent pups depend mostly on pup vocalizations. In terms of signal reception, audiometric studies show that otariids are highly sensitive to aerial sounds but slightly less sensitive to underwater sounds. Conversely, except for deep-diving elephant seals, phocids are quite sensitive to acoustic signals both in air and under water. Finally, despite differences in absolute hearing sensitivity, pinnipeds have similar masked hearing capabilities in both media, supporting the notion that cochlear mechanics determine the effects of noise on hearing.

Investigating the potential of multiwalled carbon nanotubes based zinc nanocomposite as a recognition interface towards plant pathogen detection.

PubMed

Tahir, Muhammad Ali; Hameed, Sadaf; Munawar, Anam; Amin, Imran; Mansoor, Shahid; Khan, Waheed S; Bajwa, Sadia Zafar

2017-11-01

The emergence of nanotechnology has opened new horizons for constructing efficient recognition interfaces. This is the first report where the potential of a multiwalled carbon nanotube based zinc nanocomposite (MWCNTs-Zn NPs) investigated for the detection of an agricultural pathogen i.e. Chili leaf curl betasatellite (ChLCB). Atomic force microscope analyses revealed the presence of multiwalled carbon nanotubes (MWCNTs) having a diameter of 50-100nm with zinc nanoparticles (Zn-NPs) of 25-500nm. In this system, these bunches of Zn-NPs anchored along the whole lengths of MWCNTs were used for the immobilization of probe DNA strands. The electrochemical performance of DNA biosensor was assessed in the absence and presence of the complementary DNA during cyclic and differential pulse voltammetry scans. Target binding events occurring on the interface surface patterned with single-stranded DNA was quantitatively translated into electrochemical signals due to hybridization process. In the presence of complementary target DNA, as the result of duplex formation, there was a decrease in the peak current from 1.89×10 -04 to 5.84×10 -05 A. The specificity of this electrochemical DNA biosensor was found to be three times as compared to non-complementary DNA. This material structuring technique can be extended to design interfaces for the recognition of the other plant viruses and biomolecules. Copyright © 2017 Elsevier B.V. All rights reserved.

Relative Effects of Juvenile and Adult Environmental Factors on Mate Attraction and Recognition in the Cricket, Allonemobius socius

PubMed Central

Olvido, Alexander E.; Fernandes, Pearl R.; Mousseau, Timothy A.

2010-01-01

Finding a mate is a fundamental aspect of sexual reproduction. To this end, specific-mate recognition systems (SMRS) have evolved that facilitate copulation between producers of the mating signal and their opposite-sex responders. Environmental variation, however, may compromise the efficiency with which SMRS operate. In this study, the degree to which seasonal climate experienced during juvenile and adult life-cycle stages affects the SMRS of a cricket, Allonemobius socius (Scudder) (Orthoptera: Gryllidae) was assessed. Results from two-choice behavioral trials suggest that adult ambient temperature, along with population and family origins, mediate variation in male mating call, and to a lesser extent directional response of females for those calls. Restricted maximum-likelihood estimates of heritability for male mating call components and for female response to mating call appeared statistically nonsignificant. However, appreciable “maternal genetic effects” suggest that maternal egg provisioning and other indirect maternal determinants of the embryonic environment significantly contributed to variation in male mating call and female response to mating calls. Thus, environmental factors can generate substantial variation in A. socius mating call, and, more importantly, their marginal effect on female responses to either fast-chirp or long-chirp mating calls suggest negative fitness consequences to males producing alternative types of calls. Future studies of sexual selection and SMRS evolution, particularly those focused on hybrid zone dynamics, should take explicit account of the loose concordance between signal producers and responders suggested by the current findings. PMID:20673114

Scrambled Eggs: Apoptotic Cell Clearance by Non-Professional Phagocytes in the Drosophila Ovary.

PubMed

Serizier, Sandy B; McCall, Kimberly

2017-01-01

For half of a century, it has been known that non-professional phagocytes, such as fibroblasts, endothelial, and epithelial cells, are capable of efferocytosis (engulfment of apoptotic cells). Non-professional phagocytes differ from professional phagocytes in the range and efficiency of engulfment. Much of the recognition and underlying signaling machinery between non-professional and professional phagocytes is the same, but it is not known how the engulfment capacity of non-professional phagocytes is controlled. Moreover, the signaling networks involved in cell corpse recognition, engulfment, and phagosome maturation are only partially understood. The Drosophila ovary provides an excellent system to investigate the regulation of phagocytic activity by epithelial cells, a major class of non-professional phagocytes. During Drosophila oogenesis, mid-stage egg chambers undergo apoptosis of the germline in response to nutrient deprivation. Epithelial follicle cells then undergo major cell shape changes and concomitantly engulf the germline material. Our previous work has established that Draper and the integrin α-PS3/β-PS heterodimer are required in follicle cells for germline cell clearance. In addition, we have characterized phagosome maturation pathways, and found that the JNK pathway amplifies the engulfment response. In this review, we discuss recent advances on the interplay between engulfment pathways in the follicular epithelium for cell clearance in the Drosophila ovary. We also provide a comparison to apoptotic cell clearance mechanisms in C. elegans and mammals, illustrating strong conservation of efferocytosis mechanisms by non-professional phagocytes.

Acoustic divergence in the communication of cryptic species of nocturnal primates (Microcebus ssp.)

PubMed Central

Braune, Pia; Schmidt, Sabine; Zimmermann, Elke

2008-01-01

Background A central question in evolutionary biology is how cryptic species maintain species cohesiveness in an area of sympatry. The coexistence of sympatrically living cryptic species requires the evolution of species-specific signalling and recognition systems. In nocturnal, dispersed living species, specific vocalisations have been suggested to act as an ideal premating isolation mechanism. We studied the structure and perception of male advertisement calls of three nocturnal, dispersed living mouse lemur species, the grey mouse lemur (Microcebus murinus), the golden brown mouse lemur (M. ravelobensis) and the Goodman's mouse lemur (M. lehilahytsara). The first two species occur sympatrically, the latter lives allopatrically to them. Results A multi-parameter sound analysis revealed prominent differences in the frequency contour and in the duration of advertisement calls. To test whether mouse lemurs respond specifically to calls of the different species, we conducted a playback experiment with M. murinus from the field using advertisement calls and alarm whistle calls of all three species. Individuals responded significantly stronger to conspecific than to heterospecific advertisement calls but there were no differences in response behaviour towards statistically similar whistle calls of the three species. Furthermore, sympatric calls evoked weaker interest than allopatric advertisement calls. Conclusion Our results provide the first evidence for a specific relevance of social calls for speciation in cryptic primates. They furthermore support that specific differences in signalling and recognition systems represent an efficient premating isolation mechanism contributing to species cohesiveness in sympatrically living species. PMID:18462484

Scrambled Eggs: Apoptotic Cell Clearance by Non-Professional Phagocytes in the Drosophila Ovary

PubMed Central

Serizier, Sandy B.; McCall, Kimberly

2017-01-01

For half of a century, it has been known that non-professional phagocytes, such as fibroblasts, endothelial, and epithelial cells, are capable of efferocytosis (engulfment of apoptotic cells). Non-professional phagocytes differ from professional phagocytes in the range and efficiency of engulfment. Much of the recognition and underlying signaling machinery between non-professional and professional phagocytes is the same, but it is not known how the engulfment capacity of non-professional phagocytes is controlled. Moreover, the signaling networks involved in cell corpse recognition, engulfment, and phagosome maturation are only partially understood. The Drosophila ovary provides an excellent system to investigate the regulation of phagocytic activity by epithelial cells, a major class of non-professional phagocytes. During Drosophila oogenesis, mid-stage egg chambers undergo apoptosis of the germline in response to nutrient deprivation. Epithelial follicle cells then undergo major cell shape changes and concomitantly engulf the germline material. Our previous work has established that Draper and the integrin α-PS3/β-PS heterodimer are required in follicle cells for germline cell clearance. In addition, we have characterized phagosome maturation pathways, and found that the JNK pathway amplifies the engulfment response. In this review, we discuss recent advances on the interplay between engulfment pathways in the follicular epithelium for cell clearance in the Drosophila ovary. We also provide a comparison to apoptotic cell clearance mechanisms in C. elegans and mammals, illustrating strong conservation of efferocytosis mechanisms by non-professional phagocytes. PMID:29238344

Psychometric Functions for Shortened Administrations of a Speech Recognition Approach Using Tri-Word Presentations and Phonemic Scoring

ERIC Educational Resources Information Center

Gelfand, Stanley A.; Gelfand, Jessica T.

2012-01-01

Method: Complete psychometric functions for phoneme and word recognition scores at 8 signal-to-noise ratios from -15 dB to 20 dB were generated for the first 10, 20, and 25, as well as all 50, three-word presentations of the Tri-Word or Computer Assisted Speech Recognition Assessment (CASRA) Test (Gelfand, 1998) based on the results of 12…

Real-Time Reconfigurable Adaptive Speech Recognition Command and Control Apparatus and Method

NASA Technical Reports Server (NTRS)

Salazar, George A. (Inventor); Haynes, Dena S. (Inventor); Sommers, Marc J. (Inventor)

1998-01-01

An adaptive speech recognition and control system and method for controlling various mechanisms and systems in response to spoken instructions and in which spoken commands are effective to direct the system into appropriate memory nodes, and to respective appropriate memory templates corresponding to the voiced command is discussed. Spoken commands from any of a group of operators for which the system is trained may be identified, and voice templates are updated as required in response to changes in pronunciation and voice characteristics over time of any of the operators for which the system is trained. Provisions are made for both near-real-time retraining of the system with respect to individual terms which are determined not be positively identified, and for an overall system training and updating process in which recognition of each command and vocabulary term is checked, and in which the memory templates are retrained if necessary for respective commands or vocabulary terms with respect to an operator currently using the system. In one embodiment, the system includes input circuitry connected to a microphone and including signal processing and control sections for sensing the level of vocabulary recognition over a given period and, if recognition performance falls below a given level, processing audio-derived signals for enhancing recognition performance of the system.

Gesture recognition by instantaneous surface EMG images.

PubMed

Geng, Weidong; Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Li, Jiajun

2016-11-15

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses.

EOG-sEMG Human Interface for Communication

PubMed Central

Tamura, Hiroki; Yan, Mingmin; Sakurai, Keiko; Tanno, Koichi

2016-01-01

The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as “dual-modality” for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%. PMID:27418924

Differential Regulation of Interferon Responses by Ebola and Marburg Virus VP35 Proteins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Megan R.; Liu, Gai; Mire, Chad E.

2016-02-11

Suppression of innate immune responses during filoviral infection contributes to disease severity. Ebola (EBOV) and Marburg (MARV) viruses each encode a VP35 protein that suppresses RIG-I-like receptor signaling and interferon-α/β (IFN-α/β) production by several mechanisms, including direct binding to double stranded RNA (dsRNA). Here, we demonstrate that in cell culture, MARV infection results in a greater upregulation of IFN responses as compared to EBOV infection. This correlates with differences in the efficiencies by which EBOV and MARV VP35s antagonize RIG-I signaling. Furthermore, structural and biochemical studies suggest that differential recognition of RNA elements by the respective VP35 C-terminal IFN inhibitorymore » domain (IID) rather than affinity for RNA by the respective VP35s is critical for this observation. Our studies reveal functional differences in EBOV versus MARV VP35 RNA binding that result in unexpected differences in the host response to deadly viral pathogens.« less

EOG-sEMG Human Interface for Communication.

PubMed

Tamura, Hiroki; Yan, Mingmin; Sakurai, Keiko; Tanno, Koichi

2016-01-01

The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as "dual-modality" for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%.

The immunological synapse: the gateway to the HIV reservoir

PubMed Central

Kulpa, Deanna A; Brehm, Jessica H; Fromentin, Rémi; Cooper, Anthony; Cooper, Colleen; Ahlers, Jeffrey; Chomont, Nicolas; Sékaly, Rafick-Pierre

2013-01-01

A major challenge in the development of a cure for human immunodeficiency virus (HIV) has been the incomplete understanding of the basic mechanisms underlying HIV persistence during antiretroviral therapy. It is now realized that the establishment of a latently infected reservoir refractory to immune system recognition has thus far hindered eradication efforts. Recent investigation into the innate immune response has shed light on signaling pathways downstream of the immunological synapse critical for T-cell activation and establishment of T-cell memory. This has led to the understanding that the cell-to-cell contacts observed in an immunological synapse that involve the CD4+ T cell and antigen-presenting cell or T-cell–T-cell interactions enhance efficient viral spread and facilitate the induction and maintenance of latency in HIV-infected memory T cells. This review focuses on recent work characterizing the immunological synapse and the signaling pathways involved in T-cell activation and gene regulation in the context of HIV persistence. PMID:23772628

Kruskal-Wallis-based computationally efficient feature selection for face recognition.

PubMed

Ali Khan, Sajid; Hussain, Ayyaz; Basit, Abdul; Akram, Sheeraz

2014-01-01

Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

[A new peak detection algorithm of Raman spectra].

PubMed

Jiang, Cheng-Zhi; Sun, Qiang; Liu, Ying; Liang, Jing-Qiu; An, Yan; Liu, Bing

2014-01-01

The authors proposed a new Raman peak recognition method named bi-scale correlation algorithm. The algorithm uses the combination of the correlation coefficient and the local signal-to-noise ratio under two scales to achieve Raman peak identification. We compared the performance of the proposed algorithm with that of the traditional continuous wavelet transform method through MATLAB, and then tested the algorithm with real Raman spectra. The results show that the average time for identifying a Raman spectrum is 0.51 s with the algorithm, while it is 0.71 s with the continuous wavelet transform. When the signal-to-noise ratio of Raman peak is greater than or equal to 6 (modern Raman spectrometers feature an excellent signal-to-noise ratio), the recognition accuracy with the algorithm is higher than 99%, while it is less than 84% with the continuous wavelet transform method. The mean and the standard deviations of the peak position identification error of the algorithm are both less than that of the continuous wavelet transform method. Simulation analysis and experimental verification prove that the new algorithm possesses the following advantages: no needs of human intervention, no needs of de-noising and background removal operation, higher recognition speed and higher recognition accuracy. The proposed algorithm is operable in Raman peak identification.

Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance

NASA Astrophysics Data System (ADS)

Ai, Yan-Ting; Guan, Jiao-Yue; Fei, Cheng-Wei; Tian, Jing; Zhang, Feng-Ling

2017-05-01

To monitor rolling bearing operating status with casings in real time efficiently and accurately, a fusion method based on n-dimensional characteristic parameters distance (n-DCPD) was proposed for rolling bearing fault diagnosis with two types of signals including vibration signal and acoustic emission signals. The n-DCPD was investigated based on four information entropies (singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet space characteristic spectrum entropy and wavelet energy spectrum entropy in time-frequency domain) and the basic thought of fusion information entropy fault diagnosis method with n-DCPD was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner-ball faults, inner-outer faults and normal) are collected under different operation conditions with the emphasis on the rotation speed from 800 rpm to 2000 rpm. In the light of the proposed fusion information entropy method with n-DCPD, the diagnosis of rolling bearing faults was completed. The fault diagnosis results show that the fusion entropy method holds high precision in the recognition of rolling bearing faults. The efforts of this study provide a novel and useful methodology for the fault diagnosis of an aeroengine rolling bearing.

Hybridization chain reaction amplification for highly sensitive fluorescence detection of DNA with dextran coated microarrays.

PubMed

Chao, Jie; Li, Zhenhua; Li, Jing; Peng, Hongzhen; Su, Shao; Li, Qian; Zhu, Changfeng; Zuo, Xiaolei; Song, Shiping; Wang, Lianhui; Wang, Lihua

2016-07-15

Microarrays of biomolecules hold great promise in the fields of genomics, proteomics, and clinical assays on account of their remarkably parallel and high-throughput assay capability. However, the fluorescence detection used in most conventional DNA microarrays is still limited by sensitivity. In this study, we have demonstrated a novel universal and highly sensitive platform for fluorescent detection of sequence specific DNA at the femtomolar level by combining dextran-coated microarrays with hybridization chain reaction (HCR) signal amplification. Three-dimensional dextran matrix was covalently coated on glass surface as the scaffold to immobilize DNA recognition probes to increase the surface binding capacity and accessibility. DNA nanowire tentacles were formed on the matrix surface for efficient signal amplification by capturing multiple fluorescent molecules in a highly ordered way. By quantifying microscopic fluorescent signals, the synergetic effects of dextran and HCR greatly improved sensitivity of DNA microarrays, with a detection limit of 10fM (1×10(5) molecules). This detection assay could recognize one-base mismatch with fluorescence signals dropped down to ~20%. This cost-effective microarray platform also worked well with samples in serum and thus shows great potential for clinical diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

Firing-rate resonances in the peripheral auditory system of the cricket, Gryllus bimaculatus.

PubMed

Rau, Florian; Clemens, Jan; Naumov, Victor; Hennig, R Matthias; Schreiber, Susanne

2015-11-01

In many communication systems, information is encoded in the temporal pattern of signals. For rhythmic signals that carry information in specific frequency bands, a neuronal system may profit from tuning its inherent filtering properties towards a peak sensitivity in the respective frequency range. The cricket Gryllus bimaculatus evaluates acoustic communication signals of both conspecifics and predators. The song signals of conspecifics exhibit a characteristic pulse pattern that contains only a narrow range of modulation frequencies. We examined individual neurons (AN1, AN2, ON1) in the peripheral auditory system of the cricket for tuning towards specific modulation frequencies by assessing their firing-rate resonance. Acoustic stimuli with a swept-frequency envelope allowed an efficient characterization of the cells' modulation transfer functions. Some of the examined cells exhibited tuned band-pass properties. Using simple computational models, we demonstrate how different, cell-intrinsic or network-based mechanisms such as subthreshold resonances, spike-triggered adaptation, as well as an interplay of excitation and inhibition can account for the experimentally observed firing-rate resonances. Therefore, basic neuronal mechanisms that share negative feedback as a common theme may contribute to selectivity in the peripheral auditory pathway of crickets that is designed towards mate recognition and predator avoidance.

A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation

USDA-ARS?s Scientific Manuscript database

Perception of pathogen-associated molecular patterns (PAMPs) by surface-localised pattern-recognition receptors (PRRs) is a key component of plant innate immunity. Most known plant PRRs are receptor kinases and initiation of PAMP-triggered immunity (PTI) signalling requires phosphorylation of the PR...

Distributed Recognition of Natural Songs by European Starlings

ERIC Educational Resources Information Center

Knudsen, Daniel; Thompson, Jason V.; Gentner, Timothy Q.

2010-01-01

Individual vocal recognition behaviors in songbirds provide an excellent framework for the investigation of comparative psychological and neurobiological mechanisms that support the perception and cognition of complex acoustic communication signals. To this end, the complex songs of European starlings have been studied extensively. Yet, several…

Identifying and detecting facial expressions of emotion in peripheral vision.

PubMed

Smith, Fraser W; Rossit, Stephanie

2018-01-01

Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus.

Identifying and detecting facial expressions of emotion in peripheral vision

PubMed Central

Rossit, Stephanie

2018-01-01

Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus. PMID:29847562

Decoding the patterns of ubiquitin recognition by ubiquitin-associated domains from free energy simulations.

PubMed

Bouvier, Benjamin

2014-01-07

Ubiquitin is a highly conserved, highly represented protein acting as a regulating signal in numerous cellular processes. It leverages a single hydrophobic binding patch to recognize and bind a large variety of protein domains with remarkable specificity, but can also self-assemble into chains of poly-diubiquitin units in which these interfaces are sequestered, profoundly altering the individual monomers' recognition characteristics. Despite numerous studies, the origins of this varied specificity and the competition between substrates for the binding of the ubiquitin interface patch remain under heated debate. This study uses enhanced sampling all-atom molecular dynamics to simulate the unbinding of complexes of mono- or K48-linked diubiquitin bound to several ubiquitin-associated domains, providing insights into the mechanism and free energetics of ubiquitin recognition and binding. The implications for the subtle tradeoff between the stability of the polyubiquitin signal and its easy recognition by target protein assemblies are discussed, as is the enhanced affinity of the latter for long polyubiquitin chains compared to isolated mono- or diubiquitin.

Speech Enhancement Using Gaussian Scale Mixture Models

PubMed Central

Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.

2011-01-01

This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches to enhance the efficiency: the Laplace method and a variational approximation. The proposed methods were applied to enhance speech corrupted by Gaussian noise and speech-shaped noise (SSN). For both approximations, signals reconstructed from the estimated frequency coefficients provided higher signal-to-noise ratio (SNR) and those reconstructed from the estimated log-spectra produced lower word recognition error rate because the log-spectra fit the inputs to the recognizer better. Our algorithms effectively reduced the SSN, which algorithms based on spectral analysis were not able to suppress. PMID:21359139

Protein kinase Cδ is a critical component of Dectin-1 signaling in primary human monocytes.

PubMed

Elsori, Deena H; Yakubenko, Valentin P; Roome, Talat; Thiagarajan, Praveena S; Bhattacharjee, Ashish; Yadav, Satya P; Cathcart, Martha K

2011-09-01

Zymosan, a mimic of fungal pathogens, and its opsonized form (ZOP) are potent stimulators of monocyte NADPH oxidase, resulting in the production of O(2)(.-), which is critical for host defense against fungal and bacterial pathogens and efficient immune responses; however, uncontrolled O(2)(.-) production may contribute to chronic inflammation and tissue injury. Our laboratory has focused on characterizing the signal transduction pathways that regulate NADPH oxidase activity in primary human monocytes. In this study, we examined the involvement of various pattern recognition receptors and found that Dectin-1 is the primary receptor for zymosan stimulation of O(2)(.-) via NADPH oxidase in human monocytes, whereas Dectin-1 and CR3 mediate the activation by ZOP. Further studies identified Syk and Src as important signaling components downstream of Dectin-1 and additionally identified PKCδ as a novel downstream signaling component for zymosan-induced O(2)(.-) as well as phagocytosis. Our results show that Syk and Src association with Dectin-1 is dependent on PKCδ activity and expression and demonstrate direct binding between Dectin-1 and PKCδ. Finally, our data show that PKCδ and Syk but not Src are required for Dectin-1-mediated phagocytosis. Taken together, our data identify Dectin-1 as the major PRR for zymosan in primary human monocytes and identify PKCδ as a novel downstream signaling kinase for Dectin-1-mediated regulation of monocyte NADPH oxidase and zymosan phagocytosis.

Amplifying Electrochemical Indicators

NASA Technical Reports Server (NTRS)

Fan, Wenhong; Li, Jun; Han, Jie

2004-01-01

Dendrimeric reporter compounds have been invented for use in sensing and amplifying electrochemical signals from molecular recognition events that involve many chemical and biological entities. These reporter compounds can be formulated to target specific molecules or molecular recognition events. They can also be formulated to be, variously, hydrophilic or amphiphilic so that they are suitable for use at interfaces between (1) aqueous solutions and (2) electrodes connected to external signal-processing electronic circuits. The invention of these reporter compounds is expected to enable the development of highly miniaturized, low-power-consumption, relatively inexpensive, mass-producible sensor units for diverse applications.

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

Yang, Bing; Wang, Xiaohua; Yao, Jinliang; Yang, Xin; Zhu, Wenhua

2013-10-01

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Multiview human activity recognition system based on spatiotemporal template for video surveillance system

NASA Astrophysics Data System (ADS)

Kushwaha, Alok Kumar Singh; Srivastava, Rajeev

2015-09-01

An efficient view invariant framework for the recognition of human activities from an input video sequence is presented. The proposed framework is composed of three consecutive modules: (i) detect and locate people by background subtraction, (ii) view invariant spatiotemporal template creation for different activities, (iii) and finally, template matching is performed for view invariant activity recognition. The foreground objects present in a scene are extracted using change detection and background modeling. The view invariant templates are constructed using the motion history images and object shape information for different human activities in a video sequence. For matching the spatiotemporal templates for various activities, the moment invariants and Mahalanobis distance are used. The proposed approach is tested successfully on our own viewpoint dataset, KTH action recognition dataset, i3DPost multiview dataset, MSR viewpoint action dataset, VideoWeb multiview dataset, and WVU multiview human action recognition dataset. From the experimental results and analysis over the chosen datasets, it is observed that the proposed framework is robust, flexible, and efficient with respect to multiple views activity recognition, scale, and phase variations.

Neurocomputational account of memory and perception: Thresholded and graded signals in the hippocampus.

PubMed

Elfman, Kane W; Aly, Mariam; Yonelinas, Andrew P

2014-12-01

Recent evidence suggests that the hippocampus, a region critical for long-term memory, also supports certain forms of high-level visual perception. A seemingly paradoxical finding is that, unlike the thresholded hippocampal signals associated with memory, the hippocampus produces graded, strength-based signals in perception. This article tests a neurocomputational model of the hippocampus, based on the complementary learning systems framework, to determine if the same model can account for both memory and perception, and whether it produces the appropriate thresholded and strength-based signals in these two types of tasks. The simulations showed that the hippocampus, and most prominently the CA1 subfield, produced graded signals when required to discriminate between highly similar stimuli in a perception task, but generated thresholded patterns of activity in recognition memory. A threshold was observed in recognition memory because pattern completion occurred for only some trials and completely failed to occur for others; conversely, in perception, pattern completion always occurred because of the high degree of item similarity. These results offer a neurocomputational account of the distinct hippocampal signals associated with perception and memory, and are broadly consistent with proposals that CA1 functions as a comparator of expected versus perceived events. We conclude that the hippocampal computations required for high-level perceptual discrimination are congruous with current neurocomputational models that account for recognition memory, and fit neatly into a broader description of the role of the hippocampus for the processing of complex relational information. © 2014 Wiley Periodicals, Inc.

Decoding an olfactory mechanism of kin recognition and inbreeding avoidance in a primate.

PubMed

Boulet, Marylène; Charpentier, Marie J E; Drea, Christine M

2009-12-03

Like other vertebrates, primates recognize their relatives, primarily to minimize inbreeding, but also to facilitate nepotism. Although associative, social learning is typically credited for discrimination of familiar kin, discrimination of unfamiliar kin remains unexplained. As sex-biased dispersal in long-lived species cannot consistently prevent encounters between unfamiliar kin, inbreeding remains a threat and mechanisms to avoid it beg explanation. Using a molecular approach that combined analyses of biochemical and microsatellite markers in 17 female and 19 male ring-tailed lemurs (Lemur catta), we describe odor-gene covariance to establish the feasibility of olfactory-mediated kin recognition. Despite derivation from different genital glands, labial and scrotal secretions shared about 170 of their respective 338 and 203 semiochemicals. In addition, these semiochemicals encoded information about genetic relatedness within and between the sexes. Although the sexes showed opposite seasonal patterns in signal complexity, the odor profiles of related individuals (whether same-sex or mixed-sex dyads) converged most strongly in the competitive breeding season. Thus, a strong, mutual olfactory signal of genetic relatedness appeared specifically when such information would be crucial for preventing inbreeding. That weaker signals of genetic relatedness might exist year round could provide a mechanism to explain nepotism between unfamiliar kin. We suggest that signal convergence between the sexes may reflect strong selective pressures on kin recognition, whereas signal convergence within the sexes may arise as its by-product or function independently to prevent competition between unfamiliar relatives. The link between an individual's genome and its olfactory signals could be mediated by biosynthetic pathways producing polymorphic semiochemicals or by carrier proteins modifying the individual bouquet of olfactory cues. In conclusion, we unveil a possible olfactory mechanism of kin recognition that has specific relevance to understanding inbreeding avoidance and nepotistic behavior observed in free-ranging primates, and broader relevance to understanding the mechanisms of vertebrate olfactory communication.

EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study

PubMed Central

2013-01-01

Background Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients’ intentions while attempting to generate goal-directed movements in the horizontal plane. Methods Nine right-handed healthy subjects and seven right-handed stroke survivors performed reaching movements in the horizontal plane. EMG signals were recorded and used to identify the intended motion direction of the subjects. To this aim, a standard pattern recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests were carried out to understand the role of the inter- and intra-subjects’ variability in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification were evaluated by means of an assessment index calculated from the results achieved with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE). Results Processing the EMG signals of the healthy subjects, in most of the cases we were able to build a static functional map of the EMG activation patterns for point-to-point reaching movements on the horizontal plane. On the contrary, when processing the EMG signals of the pathological subjects a good classification was not possible. In particular, patients’ aimed movement direction was not predictable with sufficient accuracy either when using the general map extracted from data of normal subjects and when tuning the classifier on the EMG signals recorded from each patient. Conclusions The experimental findings herein reported show that the use of EMG patterns recognition approach might not be practical to decode movement intention in subjects with neurological injury such as stroke. Rather than estimate motion from EMGs, future scenarios should encourage the utilization of these signals to detect and interpret the normal and abnormal muscle patterns and provide feedback on their correct recruitment. PMID:23855907

Speech Recognition Using Multiple Features and Multiple Recognizers

DTIC Science & Technology

1991-12-03

6 2.1 Introduction ............................................... 6 2.2 Human Speech Communication Process...119 How to Setup ASRT.......................................... 119 How to Use Interactive Menus .................................. 120...recognize a word from an acoustic signal. The human ear and brain perform this type of recognition with incredible speed and precision. Even though

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques

USDA-ARS?s Scientific Manuscript database

The combination of gas chromatography and pattern recognition (GC/PR) analysis is a powerful tool for investigating complicated biological problems. Clustering, mapping, discriminant development, etc. are necessary to analyze realistically large chromatographic data sets and to seek meaningful relat...

Modeling the effect of channel number and interaction on consonant recognition in a cochlear implant peak-picking strategy.

PubMed

Verschuur, Carl

2009-03-01

Difficulties in speech recognition experienced by cochlear implant users may be attributed both to information loss caused by signal processing and to information loss associated with the interface between the electrode array and auditory nervous system, including cross-channel interaction. The objective of the work reported here was to attempt to partial out the relative contribution of these different factors to consonant recognition. This was achieved by comparing patterns of consonant feature recognition as a function of channel number and presence/absence of background noise in users of the Nucleus 24 device with normal hearing subjects listening to acoustic models that mimicked processing of that device. Additionally, in the acoustic model experiment, a simulation of cross-channel spread of excitation, or "channel interaction," was varied. Results showed that acoustic model experiments were highly correlated with patterns of performance in better-performing cochlear implant users. Deficits to consonant recognition in this subgroup could be attributed to cochlear implant processing, whereas channel interaction played a much smaller role in determining performance errors. The study also showed that large changes to channel number in the Advanced Combination Encoder signal processing strategy led to no substantial changes in performance.

When fear forms memories: threat of shock and brain potentials during encoding and recognition.

PubMed

Weymar, Mathias; Bradley, Margaret M; Hamm, Alfons O; Lang, Peter J

2013-03-01

The anticipation of highly aversive events is associated with measurable defensive activation, and both animal and human research suggests that stress-inducing contexts can facilitate memory. Here, we investigated whether encoding stimuli in the context of anticipating an aversive shock affects recognition memory. Event-related potentials (ERPs) were measured during a recognition test for words that were encoded in a font color that signaled threat or safety. At encoding, cues signaling threat of shock, compared to safety, prompted enhanced P2 and P3 components. Correct recognition of words encoded in the context of threat, compared to safety, was associated with an enhanced old-new ERP difference (500-700 msec; centro-parietal), and this difference was most reliable for emotional words. Moreover, larger old-new ERP differences when recognizing emotional words encoded in a threatening context were associated with better recognition, compared to words encoded in safety. Taken together, the data indicate enhanced memory for stimuli encoded in a context in which an aversive event is merely anticipated, which could assist in understanding effects of anxiety and stress on memory processes. Copyright © 2012 Elsevier Ltd. All rights reserved.

When the face fits: recognition of celebrities from matching and mismatching faces and voices.

PubMed

Stevenage, Sarah V; Neil, Greg J; Hamlin, Iain

2014-01-01

The results of two experiments are presented in which participants engaged in a face-recognition or a voice-recognition task. The stimuli were face-voice pairs in which the face and voice were co-presented and were either "matched" (same person), "related" (two highly associated people), or "mismatched" (two unrelated people). Analysis in both experiments confirmed that accuracy and confidence in face recognition was consistently high regardless of the identity of the accompanying voice. However accuracy of voice recognition was increasingly affected as the relationship between voice and accompanying face declined. Moreover, when considering self-reported confidence in voice recognition, confidence remained high for correct responses despite the proportion of these responses declining across conditions. These results converged with existing evidence indicating the vulnerability of voice recognition as a relatively weak signaller of identity, and results are discussed in the context of a person-recognition framework.

Phosphotyrosine recognition domains: the typical, the atypical and the versatile

PubMed Central

2012-01-01

SH2 domains are long known prominent players in the field of phosphotyrosine recognition within signaling protein networks. However, over the years they have been joined by an increasing number of other protein domain families that can, at least with some of their members, also recognise pTyr residues in a sequence-specific context. This superfamily of pTyr recognition modules, which includes substantial fractions of the PTB domains, as well as much smaller, or even single member fractions like the HYB domain, the PKCδ and PKCθ C2 domains and RKIP, represents a fascinating, medically relevant and hence intensely studied part of the cellular signaling architecture of metazoans. Protein tyrosine phosphorylation clearly serves a plethora of functions and pTyr recognition domains are used in a similarly wide range of interaction modes, which encompass, for example, partner protein switching, tandem recognition functionalities and the interaction with catalytically active protein domains. If looked upon closely enough, virtually no pTyr recognition and regulation event is an exact mirror image of another one in the same cell. Thus, the more we learn about the biology and ultrastructural details of pTyr recognition domains, the more does it become apparent that nature cleverly combines and varies a few basic principles to generate a sheer endless number of sophisticated and highly effective recognition/regulation events that are, under normal conditions, elegantly orchestrated in time and space. This knowledge is also valuable when exploring pTyr reader domains as diagnostic tools, drug targets or therapeutic reagents to combat human diseases. PMID:23134684

Chaotic map clustering algorithm for EEG analysis

NASA Astrophysics Data System (ADS)

Bellotti, R.; De Carlo, F.; Stramaglia, S.

2004-03-01

The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

Numerical demonstration of neuromorphic computing with photonic crystal cavities.

PubMed

Laporte, Floris; Katumba, Andrew; Dambre, Joni; Bienstman, Peter

2018-04-02

We propose a new design for a passive photonic reservoir computer on a silicon photonics chip which can be used in the context of optical communication applications, and study it through detailed numerical simulations. The design consists of a photonic crystal cavity with a quarter-stadium shape, which is known to foster interesting mixing dynamics. These mixing properties turn out to be very useful for memory-dependent optical signal processing tasks, such as header recognition. The proposed, ultra-compact photonic crystal cavity exhibits a memory of up to 6 bits, while simultaneously accepting bitrates in a wide region of operation. Moreover, because of the inherent low losses in a high-Q photonic crystal cavity, the proposed design is very power efficient.

Implementation study of wearable sensors for activity recognition systems

PubMed Central

Ghassemian, Mona

2015-01-01

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely ‘stream-based’, ‘feature-based’ and ‘threshold-based’ scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency. PMID:26609413

Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis.

PubMed

Derpanis, Konstantinos G; Sizintsev, Mikhail; Cannons, Kevin J; Wildes, Richard P

2013-03-01

This paper provides a unified framework for the interrelated topics of action spotting, the spatiotemporal detection and localization of human actions in video, and action recognition, the classification of a given video into one of several predefined categories. A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements. This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features. Importantly, the descriptor allows for the comparison of the underlying dynamics of two spacetime video segments irrespective of spatial appearance, such as differences induced by clothing, and with robustness to clutter. An associated similarity measure is introduced that admits efficient exhaustive search for an action template, derived from a single exemplar video, across candidate video sequences. The general approach presented for action spotting and recognition is amenable to efficient implementation, which is deemed critical for many important applications. For action spotting, details of a real-time GPU-based instantiation of the proposed approach are provided. Empirical evaluation of both action spotting and action recognition on challenging datasets suggests the efficacy of the proposed approach, with state-of-the-art performance documented on standard datasets.

Laser Fluence Recognition Using Computationally Intelligent Pulsed Photoacoustics Within the Trace Gases Analysis

NASA Astrophysics Data System (ADS)

Lukić, M.; Ćojbašić, Ž.; Rabasović, M. D.; Markushev, D. D.; Todorović, D. M.

2017-11-01

In this paper, the possibilities of computational intelligence applications for trace gas monitoring are discussed. For this, pulsed infrared photoacoustics is used to investigate SF6-Ar mixtures in a multiphoton regime, assisted by artificial neural networks. Feedforward multilayer perceptron networks are applied in order to recognize both the spatial characteristics of the laser beam and the values of laser fluence Φ from the given photoacoustic signal and prevent changes. Neural networks are trained in an offline batch training regime to simultaneously estimate four parameters from theoretical or experimental photoacoustic signals: the laser beam spatial profile R(r), vibrational-to-translational relaxation time τ _{V-T} , distance from the laser beam to the absorption molecules in the photoacoustic cell r* and laser fluence Φ . The results presented in this paper show that neural networks can estimate an unknown laser beam spatial profile and the parameters of photoacoustic signals in real time and with high precision. Real-time operation, high accuracy and the possibility of application for higher intensities of radiation for a wide range of laser fluencies are factors that classify the computational intelligence approach as efficient and powerful for the in situ measurement of atmospheric pollutants.

A Smad action turnover switch operated by WW domain readers of a phosphoserine code

PubMed Central

Aragón, Eric; Goerner, Nina; Zaromytidou, Alexia-Ileana; Xi, Qiaoran; Escobedo, Albert; Massagué, Joan; Macias, Maria J.

2011-01-01

When directed to the nucleus by TGF-β or BMP signals, Smad proteins undergo cyclin-dependent kinase 8/9 (CDK8/9) and glycogen synthase kinase-3 (GSK3) phosphorylations that mediate the binding of YAP and Pin1 for transcriptional action, and of ubiquitin ligases Smurf1 and Nedd4L for Smad destruction. Here we demonstrate that there is an order of events—Smad activation first and destruction later—and that it is controlled by a switch in the recognition of Smad phosphoserines by WW domains in their binding partners. In the BMP pathway, Smad1 phosphorylation by CDK8/9 creates binding sites for the WW domains of YAP, and subsequent phosphorylation by GSK3 switches off YAP binding and adds binding sites for Smurf1 WW domains. Similarly, in the TGF-β pathway, Smad3 phosphorylation by CDK8/9 creates binding sites for Pin1 and GSK3, then adds sites to enhance Nedd4L binding. Thus, a Smad phosphoserine code and a set of WW domain code readers provide an efficient solution to the problem of coupling TGF-β signal delivery to turnover of the Smad signal transducers. PMID:21685363

Clearance of Dying Cells by Phagocytes: Mechanisms and Implications for Disease Pathogenesis.

PubMed

Fond, Aaron M; Ravichandran, Kodi S

The efficient clearance of apoptotic cells is an evolutionarily conserved process crucial for homeostasis in multicellular organisms. The clearance involves a series of steps that ultimately facilitates the recognition of the apoptotic cell by the phagocytes and the subsequent uptake and processing of the corpse. These steps include the phagocyte sensing of "find-me" signals released by the apoptotic cell, recognizing "eat-me" signals displayed on the apoptotic cell surface, and then intracellular signaling within the phagocyte to mediate phagocytic cup formation around the corpse and corpse internalization, and the processing of the ingested contents. The engulfment of apoptotic cells by phagocytes not only eliminates debris from tissues but also produces an anti-inflammatory response that suppresses local tissue inflammation. Conversely, impaired corpse clearance can result in loss of immune tolerance and the development of various inflammation-associated disorders such as autoimmunity, atherosclerosis, and airway inflammation but can also affect cancer progression. Recent studies suggest that the clearance process can also influence antitumor immune responses. In this review, we will discuss how apoptotic cells interact with their engulfing phagocytes to generate important immune responses, and how modulation of such responses can influence pathology.

Learning from the Messengers: Innate Sensing of Viruses and Cytokine Regulation of Immunity—Clues for Treatments and Vaccines

PubMed Central

Melchjorsen, Jesper

2013-01-01

Virus infections are a major global public health concern, and only via substantial knowledge of virus pathogenesis and antiviral immune responses can we develop and improve medical treatments, and preventive and therapeutic vaccines. Innate immunity and the shaping of efficient early immune responses are essential for control of viral infections. In order to trigger an efficient antiviral defense, the host senses the invading microbe via pattern recognition receptors (PRRs), recognizing distinct conserved pathogen-associated molecular patterns (PAMPs). The innate sensing of the invading virus results in intracellular signal transduction and subsequent production of interferons (IFNs) and proinflammatory cytokines. Cytokines, including IFNs and chemokines, are vital molecules of antiviral defense regulating cell activation, differentiation of cells, and, not least, exerting direct antiviral effects. Cytokines shape and modulate the immune response and IFNs are principle antiviral mediators initiating antiviral response through induction of antiviral proteins. In the present review, I describe and discuss the current knowledge on early virus–host interactions, focusing on early recognition of virus infection and the resulting expression of type I and type III IFNs, proinflammatory cytokines, and intracellular antiviral mediators. In addition, the review elucidates how targeted stimulation of innate sensors, such as toll-like receptors (TLRs) and intracellular RNA and DNA sensors, may be used therapeutically. Moreover, I present and discuss data showing how current antimicrobial therapies, including antibiotics and antiviral medication, may interfere with, or improve, immune response. PMID:23435233

An application of viola jones method for face recognition for absence process efficiency

NASA Astrophysics Data System (ADS)

Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

2018-04-01

Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

Expanding Cancer Detection Using Molecular Imprinting for a Novel Point-of-Care Diagnostic Device

NASA Astrophysics Data System (ADS)

Yu, Yingjie; Rafailovich, Miriam; Wang, Yantian; Kang, Yeona; Zhang, Lingxi; Rigas, Basil; Division of Gastroenterology, School of Medicine Team

2013-03-01

We propose the use of a potentiometric biosensor that incorporates the efficient and specific molecular imprinting (MI) method with a self-assembled monolayer (SAM). We first tested the biosensor using carcinoembryonic antigen, CEA, a biomarker associated with pancreatic cancer. No change in detection efficiency was observed, indicating that the sensor is able to discriminate for the template analyte even in concentrated solution of similar substances. In addition, we use biosensor to discriminate normal fibrinogen and damaged fibrinogen, which is critical for the detection of bleeding disorder. Computer simulations of the protein structure were performed in order to estimate the changes in morphology and determine the sensitivity of the biosensor to conformational changes in the proteins. We found that even small changes in PH can generate rotation of the surface functional groups. Yet, the results show that only when the detection and imprinting conditions are similar, robust signals occurs. Hence we concluded that both morphology and surface chemistry play a role in the recognition.

Expanding Cancer Detection Using Molecular Imprinting for a Novel Point-of-Care Diagnostic Device

NASA Astrophysics Data System (ADS)

Yu, Yingjie; Rafailovich, Miriam; Wang, Yantian; Ranjbaran, Alina; Wang, Tom; Nam, David

2012-02-01

We propose the use of a potentiometric biosensor that incorporates the efficient and specific molecular imprinting (MI) method with a self-assembled monolayer (SAM). We first tested the biosensor using carcinoembryonic antigen, CEA, a biomarker associated with pancreatic cancer. No change in detection efficiency was observed when detection was performed in the presence of 100% serum albumin, indicating that the sensor is able to discriminate for the template analyte even in concentrated solution of similar substances. Computer simulations of the protein structure were performed in order to estimate the changes in morphology and determine the sensitivity of the biosensor to conformational changes in the proteins. We found that even small changes in PH can generate rotation of the surface functional groups, without significant change in the morphology. Yet, the results show that only when the detection and imprinting conditions are similar, robust signals occurs. Hence we concluded that both morphology and surface chemistry play a role in the recognition.

RIG-I detects infection with live Listeria by sensing secreted bacterial nucleic acids

PubMed Central

Abdullah, Zeinab; Schlee, Martin; Roth, Susanne; Mraheil, Mobarak Abu; Barchet, Winfried; Böttcher, Jan; Hain, Torsten; Geiger, Sergej; Hayakawa, Yoshihiro; Fritz, Jörg H; Civril, Filiz; Hopfner, Karl-Peter; Kurts, Christian; Ruland, Jürgen; Hartmann, Gunther; Chakraborty, Trinad; Knolle, Percy A

2012-01-01

Immunity against infection with Listeria monocytogenes is not achieved from innate immune stimulation by contact with killed but requires viable Listeria gaining access to the cytosol of infected cells. It has remained ill-defined how such immune sensing of live Listeria occurs. Here, we report that efficient cytosolic immune sensing requires access of nucleic acids derived from live Listeria to the cytoplasm of infected cells. We found that Listeria released nucleic acids and that such secreted bacterial RNA/DNA was recognized by the cytosolic sensors RIG-I, MDA5 and STING thereby triggering interferon β production. Secreted Listeria nucleic acids also caused RIG-I-dependent IL-1β-production and inflammasome activation. The signalling molecule CARD9 contributed to IL-1β production in response to secreted nucleic acids. In conclusion, cytosolic recognition of secreted bacterial nucleic acids by RIG-I provides a mechanistic explanation for efficient induction of immunity by live bacteria. PMID:23064150

Two-step membrane binding by the bacterial SRP receptor enable efficient and accurate Co-translational protein targeting.

PubMed

Hwang Fu, Yu-Hsien; Huang, William Y C; Shen, Kuang; Groves, Jay T; Miller, Thomas; Shan, Shu-Ou

2017-07-28

The signal recognition particle (SRP) delivers ~30% of the proteome to the eukaryotic endoplasmic reticulum, or the bacterial plasma membrane. The precise mechanism by which the bacterial SRP receptor, FtsY, interacts with and is regulated at the target membrane remain unclear. Here, quantitative analysis of FtsY-lipid interactions at single-molecule resolution revealed a two-step mechanism in which FtsY initially contacts membrane via a Dynamic mode, followed by an SRP-induced conformational transition to a Stable mode that activates FtsY for downstream steps. Importantly, mutational analyses revealed extensive auto-inhibitory mechanisms that prevent free FtsY from engaging membrane in the Stable mode; an engineered FtsY pre-organized into the Stable mode led to indiscriminate targeting in vitro and disrupted FtsY function in vivo. Our results show that the two-step lipid-binding mechanism uncouples the membrane association of FtsY from its conformational activation, thus optimizing the balance between the efficiency and fidelity of co-translational protein targeting.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

The Molecular Origin of the MMR-dependent Apoptosis Pathway from Dynamics Analysis of MutSα-DNA Complexes

PubMed Central

Negureanu, Lacramioara; Salsbury, Freddie R.

2012-01-01

The cellular response to DNA damage signaling by MMR proteins is incompletely understood. It is generally accepted that MMR-dependent apoptosis pathway in response to DNA damage detection is independent of MMR's DNA repair function. In this study we investigate correlated motions in response to the binding of mismatched and PCL DNA fragments by MutSα, as derived from 50 ns molecular dynamics simulations. The protein dynamics in response to the mismatched and damaged DNA recognition suggests that MutSα signals their recognition through independent pathways providing evidence for the molecular origin of the MMR-dependent apoptosis. MSH2 subunit is indicated to play a key role in signaling both mismatched and damaged DNA recognition; localized and collective motions within the protein allow identifying sites on the MSH2 surface possible involved in recruiting proteins responsible for downstream events. Unlike in the mismatch complex, predicted key communication sites specific for the damage recognition are on the list of known cancer causing mutations or deletions. This confirms MSH2's role in signaling DNA-damage induced apoptosis and suggests that defects in MMR alone is sufficient to trigger tumorigenesis, supporting the experimental evidence that MMR-damage response function could protect from the early occurrence of tumors. Identifying these particular communication sites may have implications for the treatment of cancers that are not defective for MMR, but are unable to function optimally for MMR-dependent responses following DNA damage such as the case of resistance to cisplatin. PMID:22712459

Neuronal Spoken Word Recognition: The Time Course of Processing Variation in the Speech Signal

ERIC Educational Resources Information Center

Schild, Ulrike; Roder, Brigitte; Friedrich, Claudia K.

2012-01-01

Recent neurobiological studies revealed evidence for lexical representations that are not specified for the coronal place of articulation (PLACE; Friedrich, Eulitz, & Lahiri, 2006; Friedrich, Lahiri, & Eulitz, 2008). Here we tested when these types of underspecified representations influence neuronal speech recognition. In a unimodal…

Recognizing Speech under a Processing Load: Dissociating Energetic from Informational Factors

ERIC Educational Resources Information Center

Mattys, Sven L.; Brooks, Joanna; Cooke, Martin

2009-01-01

Effects of perceptual and cognitive loads on spoken-word recognition have so far largely escaped investigation. This study lays the foundations of a psycholinguistic approach to speech recognition in adverse conditions that draws upon the distinction between energetic masking, i.e., listening environments leading to signal degradation, and…

Multichannel Compression, Temporal Cues, and Audibility.

ERIC Educational Resources Information Center

Souza, Pamela E.; Turner, Christopher W.

1998-01-01

The effect of the reduction of the temporal envelope produced by multichannel compression on recognition was examined in 16 listeners with hearing loss, with particular focus on audibility of the speech signal. Multichannel compression improved speech recognition when superior audibility was provided by a two-channel compression system over linear…

17β-Estradiol and Agonism of G-protein-Coupled Estrogen Receptor Enhance Hippocampal Memory via Different Cell-Signaling Mechanisms

PubMed Central

Kim, Jaekyoon; Szinte, Julia S.; Boulware, Marissa I.

2016-01-01

The ability of 17β-estradiol (E2) to enhance hippocampal object recognition and spatial memory depends on rapid activation of extracellular signal-regulated kinase (ERK) in the dorsal hippocampus (DH). Although this activation can be mediated by the intracellular estrogen receptors ERα and ERβ, little is known about the role that the membrane estrogen receptor GPER plays in regulating ERK or E2-mediated memory formation. In this study, post-training DH infusion of the GPER agonist G-1 enhanced object recognition and spatial memory in ovariectomized female mice, whereas the GPER antagonist G-15 impaired memory, suggesting that GPER activation, like E2, promotes hippocampal memory formation. However, unlike E2, G-1 did not increase ERK phosphorylation, but instead significantly increased phosphorylation of c-Jun N-terminal kinase (JNK) in the DH. Moreover, DH infusion of the JNK inhibitor SP600125 prevented G-1 from enhancing object recognition and spatial memory, but the ERK inhibitor U0126 did not. These data suggest that GPER enhances memory via different cell-signaling mechanisms than E2. This conclusion was supported by data showing that the ability of E2 to facilitate memory and activate ERK signaling was not blocked by G-15 or SP600125, which demonstrates that the memory-enhancing effects of E2 are not dependent on JNK or GPER activation in the DH. Together, these data indicate that GPER regulates memory independently from ERα and ERβ by activating JNK signaling, rather than ERK signaling. Thus, the findings suggest that GPER in the DH may not function as an estrogen receptor to regulate object recognition and spatial memory. SIGNIFICANCE STATEMENT Although 17β-estradiol has long been known to regulate memory function, the molecular mechanisms underlying estrogenic memory modulation remain largely unknown. Here, we examined whether the putative membrane estrogen receptor GPER acts like the classical estrogen receptors, ERα and ERβ, to facilitate hippocampal memory in female mice. Although GPER activation did enhance object recognition and spatial memory, it did so by activating different cell-signaling mechanisms from ERα, ERβ, or 17β-estradiol. These data indicate that 17β-estradiol and GPER independently regulate hippocampal memory, and suggest that hippocampal GPER may not function as an estrogen receptor in the dorsal hippocampus. These findings are significant because they provide novel insights about the molecular mechanisms through which 17β-estradiol modulates hippocampal memory. PMID:26985039

17β-Estradiol and Agonism of G-protein-Coupled Estrogen Receptor Enhance Hippocampal Memory via Different Cell-Signaling Mechanisms.

PubMed

Kim, Jaekyoon; Szinte, Julia S; Boulware, Marissa I; Frick, Karyn M

2016-03-16

The ability of 17β-estradiol (E2) to enhance hippocampal object recognition and spatial memory depends on rapid activation of extracellular signal-regulated kinase (ERK) in the dorsal hippocampus (DH). Although this activation can be mediated by the intracellular estrogen receptors ERα and ERβ, little is known about the role that the membrane estrogen receptor GPER plays in regulating ERK or E2-mediated memory formation. In this study, post-training DH infusion of the GPER agonist G-1 enhanced object recognition and spatial memory in ovariectomized female mice, whereas the GPER antagonist G-15 impaired memory, suggesting that GPER activation, like E2, promotes hippocampal memory formation. However, unlike E2, G-1 did not increase ERK phosphorylation, but instead significantly increased phosphorylation of c-Jun N-terminal kinase (JNK) in the DH. Moreover, DH infusion of the JNK inhibitor SP600125 prevented G-1 from enhancing object recognition and spatial memory, but the ERK inhibitor U0126 did not. These data suggest that GPER enhances memory via different cell-signaling mechanisms than E2. This conclusion was supported by data showing that the ability of E2 to facilitate memory and activate ERK signaling was not blocked by G-15 or SP600125, which demonstrates that the memory-enhancing effects of E2 are not dependent on JNK or GPER activation in the DH. Together, these data indicate that GPER regulates memory independently from ERα and ERβ by activating JNK signaling, rather than ERK signaling. Thus, the findings suggest that GPER in the DH may not function as an estrogen receptor to regulate object recognition and spatial memory. Although 17β-estradiol has long been known to regulate memory function, the molecular mechanisms underlying estrogenic memory modulation remain largely unknown. Here, we examined whether the putative membrane estrogen receptor GPER acts like the classical estrogen receptors, ERα and ERβ, to facilitate hippocampal memory in female mice. Although GPER activation did enhance object recognition and spatial memory, it did so by activating different cell-signaling mechanisms from ERα, ERβ, or 17β-estradiol. These data indicate that 17β-estradiol and GPER independently regulate hippocampal memory, and suggest that hippocampal GPER may not function as an estrogen receptor in the dorsal hippocampus. These findings are significant because they provide novel insights about the molecular mechanisms through which 17β-estradiol modulates hippocampal memory. Copyright © 2016 the authors 0270-6474/16/363309-13$15.00/0.

[Research on Control System of an Exoskeleton Upper-limb Rehabilitation Robot].

PubMed

Wang, Lulu; Hu, Xin; Hu, Jie; Fang, Youfang; He, Rongrong; Yu, Hongliu

2016-12-01

In order to help the patients with upper-limb disfunction go on rehabilitation training,this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom(DOF),and realized two control schemes,i.e.,voice control and electromyography control.The hardware and software design of the voice control system was completed based on RSC-4128 chips,which realized the speech recognition technology of a specific person.Besides,this study adapted self-made surface eletromyogram(sEMG)signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing,extracting time domain features and fixed threshold algorithm.In addition,the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system.Voice control and electromyography control experiments were then carried out,and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1%and 90.9%,respectively.The results proved the feasibility of the control system.This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.

Male mate recognition via cuticular hydrocarbons facilitates sexual isolation between sympatric leaf beetle sister species.

PubMed

Zhang, Bin; Xue, Huai-Jun; Song, Ke-Qing; Liu, Jie; Li, Wen-Zhu; Nie, Rui-E; Yang, Xing-Ke

2014-11-01

Chemical signals in insects have been documented to play an important role in mate recognition, and divergence in chemical signals can often cause sexual isolation between closely related species or populations within species. We investigated the role of cuticular hydrocarbons (CHCs), short distance chemical signals, in male mate recognition between the two sympatric elm leaf beetles, Pyrrhalta maculicollis and Pyrrhaltaaenescens. Mating experiments demonstrated that strong sexual isolation between the two species was driven by CHCs divergence. Males preferred to mate with conspecific females with intact conspecific CHCs or conspecific CHCs reapplied after removal. Males also preferred heterospecific females that were treated with conspecific CHCs. Chemical analysis showed that the CHC profiles differ significantly between species. In P. maculicollis dimethyl-branched alkanes between C29 and C35 account for the majority of the saturated alkanes while the CHC profile of P. aenescens mostly consisted of monomethyl-branched alkanes between C22 and C29. Additionally, some compounds, such as 12,18-diMeC32, 12,18-diMeC34, are unique to P. maculicollis. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Effects of pre-experimental knowledge on recognition memory.

PubMed

Bird, Chris M; Davies, Rachel A; Ward, Jamie; Burgess, Neil

2011-01-01

The influence of pre-experimental autobiographical knowledge on recognition memory was investigated using as memoranda faces that were either personally known or unknown to the participant. Under a dual process theory, such knowledge boosted both recollection- and familiarity-based recognition judgements. Under an unequal variance signal detection model, pre-experimental knowledge increased both the variance and the separation of the target and foil memory strength distributions, boosting hits and correct rejections. Thus, pre-experimental knowledge has profound effects on the multiple, interacting processes that subserve recognition memory, and likely in the neural systems that underpin them.

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

PubMed

Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

2016-09-22

Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the realization of a practical EEG-based emotion recognition system.

Experimental study on GMM-based speaker recognition

NASA Astrophysics Data System (ADS)

Ye, Wenxing; Wu, Dapeng; Nucci, Antonio

2010-04-01

Speaker recognition plays a very important role in the field of biometric security. In order to improve the recognition performance, many pattern recognition techniques have be explored in the literature. Among these techniques, the Gaussian Mixture Model (GMM) is proved to be an effective statistic model for speaker recognition and is used in most state-of-the-art speaker recognition systems. The GMM is used to represent the 'voice print' of a speaker through modeling the spectral characteristic of speech signals of the speaker. In this paper, we implement a speaker recognition system, which consists of preprocessing, Mel-Frequency Cepstrum Coefficients (MFCCs) based feature extraction, and GMM based classification. We test our system with TIDIGITS data set (325 speakers) and our own recordings of more than 200 speakers; our system achieves 100% correct recognition rate. Moreover, we also test our system under the scenario that training samples are from one language but test samples are from a different language; our system also achieves 100% correct recognition rate, which indicates that our system is language independent.

A MUSIC-based method for SSVEP signal processing.

PubMed

Chen, Kun; Liu, Quan; Ai, Qingsong; Zhou, Zude; Xie, Sheng Quan; Meng, Wei

2016-03-01

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

The Systems Engineering Design of a Smart Forward Operating Base Surveillance System for Forward Operating Base Protection

DTIC Science & Technology

2013-06-01

fixed sensors located along the perimeter of the FOB. The video is analyzed for facial recognition to alert the Network Operations Center (NOC...the UAV is processed on board for facial recognition and video for behavior analysis is sent directly to the Network Operations Center (NOC). Video...captured by the fixed sensors are sent directly to the NOC for facial recognition and behavior analysis processing. The multi- directional signal

Chemical recognition of gases and gas mixtures with terahertz waves.

PubMed

Jacobsen, R H; Mittleman, D M; Nuss, M C

1996-12-15

A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classif ication of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

Chemical recognition of gases and gas mixtures with terahertz waves

NASA Astrophysics Data System (ADS)

Jacobsen, R. H.; Mittleman, D. M.; Nuss, M. C.

1996-12-01

A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classification of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

Door latching recognition apparatus and process

DOEpatents

Eakle, Jr., Robert F.

2012-05-15

An acoustic door latch detector is provided in which a sound recognition sensor is integrated into a door or door lock mechanism. The programmable sound recognition sensor can be trained to recognize the acoustic signature of the door and door lock mechanism being properly engaged and secured. The acoustic sensor will signal a first indicator indicating that proper closure was detected or sound an alarm condition if the proper acoustic signature is not detected within a predetermined time interval.

The gram-negative sensing receptor PGRP-LC contributes to grooming induction in Drosophila

PubMed Central

Neyen, Claudine; Lemaitre, Bruno; Marion-Poll, Frédéric

2017-01-01

Behavioral resistance protects insects from microbial infection. However, signals inducing insect hygiene behavior are still relatively unexplored. Our previous study demonstrated that olfactory signals from microbes enhance insect hygiene behavior, and gustatory signals even induce the behavior. In this paper, we postulated a cross-talk between behavioral resistance and innate immunity. To examine this hypothesis, we employed a previously validated behavioral test to examine the function of taste signals in inducing a grooming reflex in decapitated flies. Microbes, which activate different pattern recognition systems upstream of immune pathways, were applied to see if there was any correlation between microbial perception and grooming reflex. To narrow down candidate elicitors, the grooming induction tests were conducted with highly purified bacterial components. Lastly, the role of DAP-type peptidoglycan in grooming induction was confirmed. Our results demonstrate that cleaning behavior can be triggered through recognition of DAP-type PGN by its receptor PGRP-LC. PMID:29121087

Application of lifting wavelet and random forest in compound fault diagnosis of gearbox

NASA Astrophysics Data System (ADS)

Chen, Tang; Cui, Yulian; Feng, Fuzhou; Wu, Chunzhi

2018-03-01

Aiming at the weakness of compound fault characteristic signals of a gearbox of an armored vehicle and difficult to identify fault types, a fault diagnosis method based on lifting wavelet and random forest is proposed. First of all, this method uses the lifting wavelet transform to decompose the original vibration signal in multi-layers, reconstructs the multi-layer low-frequency and high-frequency components obtained by the decomposition to get multiple component signals. Then the time-domain feature parameters are obtained for each component signal to form multiple feature vectors, which is input into the random forest pattern recognition classifier to determine the compound fault type. Finally, a variety of compound fault data of the gearbox fault analog test platform are verified, the results show that the recognition accuracy of the fault diagnosis method combined with the lifting wavelet and the random forest is up to 99.99%.

Proteome and Secretome Analysis Reveals Differential Post-transcriptional Regulation of Toll-like Receptor Responses*

PubMed Central

Koppenol-Raab, Marijke; Sjoelund, Virginie; Manes, Nathan P.; Gottschalk, Rachel A.; Dutta, Bhaskar; Benet, Zachary L.; Fraser, Iain D. C.

2017-01-01

The innate immune system is the organism's first line of defense against pathogens. Pattern recognition receptors (PRRs) are responsible for sensing the presence of pathogen-associated molecules. The prototypic PRRs, the membrane-bound receptors of the Toll-like receptor (TLR) family, recognize pathogen-associated molecular patterns (PAMPs) and initiate an innate immune response through signaling pathways that depend on the adaptor molecules MyD88 and TRIF. Deciphering the differences in the complex signaling events that lead to pathogen recognition and initiation of the correct response remains challenging. Here we report the discovery of temporal changes in the protein signaling components involved in innate immunity. Using an integrated strategy combining unbiased proteomics, transcriptomics and macrophage stimulations with three different PAMPs, we identified differences in signaling between individual TLRs and revealed specifics of pathway regulation at the protein level. PMID:28235783

Conventional and Non-Conventional Drosophila Toll Signaling

PubMed Central

Lindsay, Scott A.; Wasserman, Steven A.

2013-01-01

The discovery of Toll in Drosophila and of the remarkable conservation in pathway composition and organization catalyzed a transformation in our understanding of innate immune recognition and response. At the center of that picture is a cascade of interactions in which specific microbial cues activate Toll receptors, which then transmit signals driving transcription factor nuclear localization and activity. Experiments gave substance to the vision of pattern recognition receptors, linked phenomena in development, gene regulation, and immunity into a coherent whole, and revealed a rich set of variations for identifying non-self and responding effectively. More recently, research in Drosophila has illuminated the positive and negative regulation of Toll activation, the organization of signaling events at and beneath membranes, the sorting of information flow, and the existence of non-conventional signaling via Toll-related receptors. Here, we provide an overview of the Toll pathway of flies and highlight these ongoing realms of research. PMID:23632253

Gesture recognition by instantaneous surface EMG images

PubMed Central

Geng, Weidong; Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Li, Jiajun

2016-01-01

Gesture recognition in non-intrusive muscle-computer interfaces is usually based on windowed descriptive and discriminatory surface electromyography (sEMG) features because the recorded amplitude of a myoelectric signal may rapidly fluctuate between voltages above and below zero. Here, we present that the patterns inside the instantaneous values of high-density sEMG enables gesture recognition to be performed merely with sEMG signals at a specific instant. We introduce the concept of an sEMG image spatially composed from high-density sEMG and verify our findings from a computational perspective with experiments on gesture recognition based on sEMG images with a classification scheme of a deep convolutional network. Without any windowed features, the resultant recognition accuracy of an 8-gesture within-subject test reached 89.3% on a single frame of sEMG image and reached 99.0% using simple majority voting over 40 frames with a 1,000 Hz sampling rate. Experiments on the recognition of 52 gestures of NinaPro database and 27 gestures of CSL-HDEMG database also validated that our approach outperforms state-of-the-arts methods. Our findings are a starting point for the development of more fluid and natural muscle-computer interfaces with very little observational latency. For example, active prostheses and exoskeletons based on high-density electrodes could be controlled with instantaneous responses. PMID:27845347

MHC odours are not required or sufficient for recognition of individual scent owners

PubMed Central

Hurst, Jane L; Thom, Michael D; Nevison, Charlotte M; Humphries, Richard E; Beynon, Robert J

2005-01-01

To provide information about specific depositors, scent marks need to encode a stable signal of individual ownership. The highly polymorphic major histocompatibility complex (MHC) influences scents and contributes to the recognition of close kin and avoidance of inbreeding when MHC haplotypes are shared. MHC diversity between individuals has also been proposed as a primary source of scents used in individual recognition. We tested this in the context of scent owner recognition among male mice, which scent mark their territories and countermark scents from other males. We examined responses towards urine scent according to the scent owner's genetic difference to the territory owner (MHC, genetic background, both and neither) or genetic match to a familiar neighbour. While urine of a different genetic background from the subject always stimulated greater scent marking than own, regardless of familiarity, MHC-associated odours were neither necessary nor sufficient for scent owner recognition and failed to stimulate countermarking. Urine of a different MHC type to the subject stimulated increased investigation only when this matched both the MHC and genetic background of a familiar neighbour. We propose an associative model of scent owner recognition in which volatile scent profiles, contributed by both fixed genetic and varying non-genetic factors, are learnt in association with a stable involatile ownership signal provided by other highly polymorphic urine components. PMID:15906464

Evidence for the contribution of a threshold retrieval process to semantic memory.

PubMed

Kempnich, Maria; Urquhart, Josephine A; O'Connor, Akira R; Moulin, Chris J A

2017-10-01

It is widely held that episodic retrieval can recruit two processes: a threshold context retrieval process (recollection) and a continuous signal strength process (familiarity). Conversely the processes recruited during semantic retrieval are less well specified. We developed a semantic task analogous to single-item episodic recognition to interrogate semantic recognition receiver-operating characteristics (ROCs) for a marker of a threshold retrieval process. We fitted observed ROC points to three signal detection models: two models typically used in episodic recognition (unequal variance and dual-process signal detection models) and a novel dual-process recollect-to-reject (DP-RR) signal detection model that allows a threshold recollection process to aid both target identification and lure rejection. Given the nature of most semantic questions, we anticipated the DP-RR model would best fit the semantic task data. Experiment 1 (506 participants) provided evidence for a threshold retrieval process in semantic memory, with overall best fits to the DP-RR model. Experiment 2 (316 participants) found within-subjects estimates of episodic and semantic threshold retrieval to be uncorrelated. Our findings add weight to the proposal that semantic and episodic memory are served by similar dual-process retrieval systems, though the relationship between the two threshold processes needs to be more fully elucidated.

Non-self recognition, transcriptional reprogramming, and secondary metabolite accumulation during plant/pathogen interactions

PubMed Central

Hahlbrock, Klaus; Bednarek, Pawel; Ciolkowski, Ingo; Hamberger, Björn; Heise, Andreas; Liedgens, Hiltrud; Logemann, Elke; Nürnberger, Thorsten; Schmelzer, Elmon; Somssich, Imre E.; Tan, Jianwen

2003-01-01

Disease resistance of plants involves two distinct forms of chemical communication with the pathogen: recognition and defense. Both are essential components of a highly complex, multifaceted defense response, which begins with non-self recognition through the perception of pathogen-derived signal molecules and results in the production, inter alia, of antibiotically active compounds (phytoalexins) and cell wall-reinforcing material around the infection site. To elucidate the molecular details and the genomic basis of the underlying chains of events, we used two different experimental systems: suspension-cultured cells of Petroselinum crispum (parsley) and wild-type as well as mutant plants of Arabidopsis thaliana. Particular emphasis was placed on the structural and functional identification of signal and defense molecules, and on the mechanisms of signal perception, intracellular signal transduction and transcriptional reprogramming, including the structural and functional characterization of the responsible cis-acting gene promoter elements and transacting regulatory proteins. Comparing P. crispum and A. thaliana allows us to distinguish species-specific defense mechanisms from more universal responses, and furthermore provides general insights into the nature of the interactions. Despite the complexity of the pathogen defense response, it is experimentally tractable, and knowledge gained so far has opened up a new realm of gene technology-assisted strategies for resistance breeding of crop plants. PMID:12704242

Fluorogenic PNA probes

PubMed Central

2018-01-01

Fluorogenic oligonucleotide probes that can produce a change in fluorescence signal upon binding to specific biomolecular targets, including nucleic acids as well as non-nucleic acid targets, such as proteins and small molecules, have applications in various important areas. These include diagnostics, drug development and as tools for studying biomolecular interactions in situ and in real time. The probes usually consist of a labeled oligonucleotide strand as a recognition element together with a mechanism for signal transduction that can translate the binding event into a measurable signal. While a number of strategies have been developed for the signal transduction, relatively little attention has been paid to the recognition element. Peptide nucleic acids (PNA) are DNA mimics with several favorable properties making them a potential alternative to natural nucleic acids for the development of fluorogenic probes, including their very strong and specific recognition and excellent chemical and biological stabilities in addition to their ability to bind to structured nucleic acid targets. In addition, the uncharged backbone of PNA allows for other unique designs that cannot be performed with oligonucleotides or analogues with negatively-charged backbones. This review aims to introduce the principle, showcase state-of-the-art technologies and update recent developments in the areas of fluorogenic PNA probes during the past 20 years. PMID:29507634

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

Recognizing familiar objects by hand and foot: Haptic shape perception generalizes to inputs from unusual locations and untrained body parts.

PubMed

Lawson, Rebecca

2014-02-01

The limits of generalization of our 3-D shape recognition system to identifying objects by touch was investigated by testing exploration at unusual locations and using untrained effectors. In Experiments 1 and 2, people found identification by hand of real objects, plastic 3-D models of objects, and raised line drawings placed in front of themselves no easier than when exploration was behind their back. Experiment 3 compared one-handed, two-handed, one-footed, and two-footed haptic object recognition of familiar objects. Recognition by foot was slower (7 vs. 13 s) and much less accurate (9 % vs. 47 % errors) than recognition by either one or both hands. Nevertheless, item difficulty was similar across hand and foot exploration, and there was a strong correlation between an individual's hand and foot performance. Furthermore, foot recognition was better with the largest 20 of the 80 items (32 % errors), suggesting that physical limitations hampered exploration by foot. Thus, object recognition by hand generalized efficiently across the spatial location of stimuli, while object recognition by foot seemed surprisingly good given that no prior training was provided. Active touch (haptics) thus efficiently extracts 3-D shape information and accesses stored representations of familiar objects from novel modes of input.

Light-Regulated Electrochemical Sensor Array for Efficiently Discriminating Hazardous Gases.

PubMed

Liang, Hongqiu; Zhang, Xin; Sun, Huihui; Jin, Han; Zhang, Xiaowei; Jin, Qinghui; Zou, Jie; Haick, Hossam; Jian, Jiawen

2017-10-27

Inadequate detection limit and unsatisfactory discrimination features remain the challenging issues for the widely applied electrochemical gas sensors. Quite recently, we confirmed that light-regulated electrochemical reaction significantly enhanced the electrocatalytic activity, and thereby can potentially extend the detection limit to the parts per billion (ppb) level. Nevertheless, impact of the light-regulated electrochemical reaction on response selectivity has been discussed less. Herein, we systematically report on the effect of illumination on discrimination features via design and fabrication of a light-regulated electrochemical sensor array. Upon illumination (light on), response signal to the examined gases (C 3 H 6 , NO, and CO) is selectively enhanced, resulting in the sensor array demonstrating disparate response patterns when compared with that of the sensor array operated at light off. Through processing all the response patterns derived from both light on and light off with a pattern recognition algorithm, a satisfactory discrimination feature is observed. In contrast, apparent mutual interference between NO and CO is found when the sensor array is solely operated without illumination. The impact mechanism of the illumination is studied and it is deduced that the effect of the illumination on the discriminating features can be mainly attributed to the competition of electrocatalytic activity and gas-phase reactivity. If the enhanced electrocatalytic activity (to specific gas) dominates the whole sensing progress, enhancements in the corresponding response signal would be observed upon illumination. Otherwise, illumination gives a negligible impact. Hence, the response signal to part of the examined gases is selectively enhanced by illumination. Conclusively, light-regulated electrochemical reaction would provide an efficient approach to designing future smart sensing devices.

Signal Detection with Criterion Noise: Applications to Recognition Memory

ERIC Educational Resources Information Center

Benjamin, Aaron S.; Diaz, Michael; Wee, Serena

2009-01-01

A tacit but fundamental assumption of the theory of signal detection is that criterion placement is a noise-free process. This article challenges that assumption on theoretical and empirical grounds and presents the noisy decision theory of signal detection (ND-TSD). Generalized equations for the isosensitivity function and for measures of…

A Complex mTOR Response in Habituation Paradigms for a Social Signal in Adult Songbirds

ERIC Educational Resources Information Center

Ahmadiantehrani, Somayeh; Gores, Elisa O.; London, Sarah E.

2018-01-01

Nonassociative learning is considered simple because it depends on presentation of a single stimulus, but it likely reflects complex molecular signaling. To advance understanding of the molecular mechanisms of one form of nonassociative learning, habituation, for ethologically relevant signals we examined song recognition learning in adult zebra…

Application of pattern recognition techniques to acousto-ultrasonic testing of Kevlar composite panels

NASA Astrophysics Data System (ADS)

Hinton, Yolanda L.

An acousto-ultrasonic evaluation of panels fabricated from woven Kevlar and PVB/phenolic resin is being conducted. The panels were fabricated with various simulated defects. They were examined by pulsing with one acoustic emission sensor, and detecting the signal with another sensor, on the same side of the panel at a fixed distance. The acoustic emission signals were filtered through high (400-600 KHz), low (100-300 KHz) and wide (100-1200 KHz) bandpass filters. Acoustic emission signal parameters, including amplitude, counts, rise time, duration, 'energy', rms, and counts to peak, were recorded. These were statistically analyzed to determine which of the AE parameters best characterize the simulated defects. The wideband filtered acoustic emission signal was also digitized and recorded for further processing. Seventy-one features of the signals in both the time and frequency domains were calculated and compared to determine which subset of these features uniquely characterize the defects in the panels. The objective of the program is to develop a database of AE signal parameters and features to be used in pattern recognition as an inspection tool for material fabricated from these materials.

Modular Activating Receptors in Innate and Adaptive Immunity.

PubMed

Berry, Richard; Call, Matthew E

2017-03-14

Triggering of cell-mediated immunity is largely dependent on the recognition of foreign or abnormal molecules by a myriad of cell surface-bound receptors. Many activating immune receptors do not possess any intrinsic signaling capacity but instead form noncovalent complexes with one or more dimeric signaling modules that communicate with a common set of kinases to initiate intracellular information-transfer pathways. This modular architecture, where the ligand binding and signaling functions are detached from one another, is a common theme that is widely employed throughout the innate and adaptive arms of immune systems. The evolutionary advantages of this highly adaptable platform for molecular recognition are visible in the variety of ligand-receptor interactions that can be linked to common signaling pathways, the diversification of receptor modules in response to pathogen challenges, and the amplification of cellular responses through incorporation of multiple signaling motifs. Here we provide an overview of the major classes of modular activating immune receptors and outline the current state of knowledge regarding how these receptors assemble, recognize their ligands, and ultimately trigger intracellular signal transduction pathways that activate immune cell effector functions.

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

NASA Astrophysics Data System (ADS)

Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

Research on pre-processing of QR Code

NASA Astrophysics Data System (ADS)

Sun, Haixing; Xia, Haojie; Dong, Ning

2013-10-01

QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese characters, etc. In order to obtain the clearer binarization image from complex background, and improve the recognition rate of QR code, this paper researches on pre-processing methods of QR code (Quick Response Code), and shows algorithms and results of image pre-processing for QR code recognition. Improve the conventional method by changing the Souvola's adaptive text recognition method. Additionally, introduce the QR code Extraction which adapts to different image size, flexible image correction approach, and improve the efficiency and accuracy of QR code image processing.

Visual environment recognition for robot path planning using template matched filters

NASA Astrophysics Data System (ADS)

Orozco-Rosas, Ulises; Picos, Kenia; Díaz-Ramírez, Víctor H.; Montiel, Oscar; Sepúlveda, Roberto

2017-08-01

A visual approach in environment recognition for robot navigation is proposed. This work includes a template matching filtering technique to detect obstacles and feasible paths using a single camera to sense a cluttered environment. In this problem statement, a robot can move from the start to the goal by choosing a single path between multiple possible ways. In order to generate an efficient and safe path for mobile robot navigation, the proposal employs a pseudo-bacterial potential field algorithm to derive optimal potential field functions using evolutionary computation. Simulation results are evaluated in synthetic and real scenes in terms of accuracy of environment recognition and efficiency of path planning computation.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Binary ROCs in Perception and Recognition Memory Are Curved

ERIC Educational Resources Information Center

Dube, Chad; Rotello, Caren M.

2012-01-01

In recognition memory, a classic finding is that receiver operating characteristics (ROCs) are curvilinear. This has been taken to support the fundamental assumptions of signal detection theory (SDT) over discrete-state models such as the double high-threshold model (2HTM), which predicts linear ROCs. Recently, however, Broder and Schutz (2009)…

Beyond ROC Curvature: Strength Effects and Response Time Data Support Continuous-Evidence Models of Recognition Memory

ERIC Educational Resources Information Center

Dube, Chad; Starns, Jeffrey J.; Rotello, Caren M.; Ratcliff, Roger

2012-01-01

A classic question in the recognition memory literature is whether retrieval is best described as a continuous-evidence process consistent with signal detection theory (SDT), or a threshold process consistent with many multinomial processing tree (MPT) models. Because receiver operating characteristics (ROCs) based on confidence ratings are…

Ignoring Memory Hints: The Stubborn Influence of Environmental Cues on Recognition Memory

ERIC Educational Resources Information Center

Selmeczy, Diana; Dobbins, Ian G.

2017-01-01

Recognition judgments can benefit from the use of environmental cues that signal the general likelihood of encountering familiar versus unfamiliar stimuli. While incorporating such cues is often adaptive, there are circumstances (e.g., eyewitness testimony) in which observers should fully ignore environmental cues in order to preserve memory…

Complex auditory behaviour emerges from simple reactive steering

NASA Astrophysics Data System (ADS)

Hedwig, Berthold; Poulet, James F. A.

2004-08-01

The recognition and localization of sound signals is fundamental to acoustic communication. Complex neural mechanisms are thought to underlie the processing of species-specific sound patterns even in animals with simple auditory pathways. In female crickets, which orient towards the male's calling song, current models propose pattern recognition mechanisms based on the temporal structure of the song. Furthermore, it is thought that localization is achieved by comparing the output of the left and right recognition networks, which then directs the female to the pattern that most closely resembles the species-specific song. Here we show, using a highly sensitive method for measuring the movements of female crickets, that when walking and flying each sound pulse of the communication signal releases a rapid steering response. Thus auditory orientation emerges from reactive motor responses to individual sound pulses. Although the reactive motor responses are not based on the song structure, a pattern recognition process may modulate the gain of the responses on a longer timescale. These findings are relevant to concepts of insect auditory behaviour and to the development of biologically inspired robots performing cricket-like auditory orientation.

Feature Selection for Nonstationary Data: Application to Human Recognition Using Medical Biometrics.

PubMed

Komeili, Majid; Louis, Wael; Armanfard, Narges; Hatzinakos, Dimitrios

2018-05-01

Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

Data-driven approach to human motion modeling with Lua and gesture description language

NASA Astrophysics Data System (ADS)

Hachaj, Tomasz; Koptyra, Katarzyna; Ogiela, Marek R.

2017-03-01

The aim of this paper is to present the novel proposition of the human motion modelling and recognition approach that enables real time MoCap signal evaluation. By motions (actions) recognition we mean classification. The role of this approach is to propose the syntactic description procedure that can be easily understood, learnt and used in various motion modelling and recognition tasks in all MoCap systems no matter if they are vision or wearable sensor based. To do so we have prepared extension of Gesture Description Language (GDL) methodology that enables movements description and real-time recognition so that it can use not only positional coordinates of body joints but virtually any type of discreetly measured output MoCap signals like accelerometer, magnetometer or gyroscope. We have also prepared and evaluated the cross-platform implementation of this approach using Lua scripting language and JAVA technology. This implementation is called Data Driven GDL (DD-GDL). In tested scenarios the average execution speed is above 100 frames per second which is an acquisition time of many popular MoCap solutions.

Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

NASA Astrophysics Data System (ADS)

Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

Diffused holographic information storage and retrieval using photorefractive optical materials

NASA Astrophysics Data System (ADS)

McMillen, Deanna Kay

Holography offers a tremendous opportunity for dense information storage, theoretically one bit per cubic wavelength of material volume, with rapid retrieval, of up to thousands of pages of information simultaneously. However, many factors prevent the theoretical storage limit from being reached, including dynamic range problems and imperfections in recording materials. This research explores new ways of moving closer to practical holographic information storage and retrieval by altering the recording materials, in this case, photorefractive crystals, and by increasing the current storage capacity while improving the information retrieved. As an experimental example of the techniques developed, the information retrieved is the correlation peak from an optical recognition architecture, but the materials and methods developed are applicable to many other holographic information storage systems. Optical correlators can potentially solve any signal or image recognition problem. Military surveillance, fingerprint identification for law enforcement or employee identification, and video games are but a few examples of applications. A major obstacle keeping optical correlators from being universally accepted is the lack of a high quality, thick (high capacity) holographic recording material that operates with red or infrared wavelengths which are available from inexpensive diode lasers. This research addresses the problems from two positions: find a better material for use with diode lasers, and reduce the requirements placed on the material while maintaining an efficient and effective system. This research found that the solutions are new dopants introduced into photorefractive lithium niobate to improve wavelength sensitivities and the use of a novel inexpensive diffuser that reduces the dynamic range and optical element quality requirements (which reduces the cost) while improving performance. A uniquely doped set of 12 lithium niobate crystals was specified and procured for this research. Transmission spectra and diffraction efficiencies were measured for each of the crystals using wavelengths in the visible spectrum. The diffraction efficiency was increased by as much as two orders of magnitude by using a new dopant combination. A new optical diffuser was designed, modeled, fabricated, and tested as a means of improving storage capacity for angularly multiplexed holograms in photorefractive crystals. The diffuser reduced the dynamic range requirement by over three orders of magnitude, increased the storage capacity by more than 400%, and dramatically improved the correlation signals.

Automatic recognition of postural allocations.

PubMed

Sazonov, Edward; Krishnamurthy, Vidya; Makeyev, Oleksandr; Browning, Ray; Schutz, Yves; Hill, James

2007-01-01

A significant part of daily energy expenditure may be attributed to non-exercise activity thermogenesis and exercise activity thermogenesis. Automatic recognition of postural allocations such as standing or sitting can be used in behavioral modification programs aimed at minimizing static postures. In this paper we propose a shoe-based device and related pattern recognition methodology for recognition of postural allocations. Inexpensive technology allows implementation of this methodology as a part of footwear. The experimental results suggest high efficiency and reliability of the proposed approach.

A vision-based automated guided vehicle system with marker recognition for indoor use.

PubMed

Lee, Jeisung; Hyun, Chang-Ho; Park, Mignon

2013-08-07

We propose an intelligent vision-based Automated Guided Vehicle (AGV) system using fiduciary markers. In this paper, we explore a low-cost, efficient vehicle guiding method using a consumer grade web camera and fiduciary markers. In the proposed method, the system uses fiduciary markers with a capital letter or triangle indicating direction in it. The markers are very easy to produce, manipulate, and maintain. The marker information is used to guide a vehicle. We use hue and saturation values in the image to extract marker candidates. When the known size fiduciary marker is detected by using a bird's eye view and Hough transform, the positional relation between the marker and the vehicle can be calculated. To recognize the character in the marker, a distance transform is used. The probability of feature matching was calculated by using a distance transform, and a feature having high probability is selected as a captured marker. Four directional signals and 10 alphabet features are defined and used as markers. A 98.87% recognition rate was achieved in the testing phase. The experimental results with the fiduciary marker show that the proposed method is a solution for an indoor AGV system.

Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms.

PubMed

Carrault, G; Cordier, M-O; Quiniou, R; Wang, F

2003-07-01

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable.

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

PubMed Central

Chen, Bin; Wang, Yanan; Yan, Zhaoli

2018-01-01

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method. PMID:29382144

Correlation-based pattern recognition for implantable defibrillators.

PubMed Central

Wilkins, J.

1996-01-01

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

NASA Astrophysics Data System (ADS)

Mohammed Anzar, Sharafudeen Thaha; Sathidevi, Puthumangalathu Savithri

2014-12-01

In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics. An efficient matching score preprocessing technique based on multi-normalization is employed for improving the performance of the multimodal system, under various noise conditions. Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation. Reliability (dispersion) and the separability (inter-/intra-class distance and d-prime statistics) measures under various noise conditions are estimated from the individual modalities, during the training/validation stage. The `best integration weights' are then computed by algebraically combining these measures using the weighted sum rule. The computed integration weights are then optimized against the recognition accuracy using techniques such as grid search, genetic algorithm and particle swarm optimization. The experimental results show that, the proposed biometric solution leads to considerable improvement in the recognition performance even under low signal-to-noise ratio (SNR) conditions and reduces the false acceptance rate (FAR) and false rejection rate (FRR), making the system useful for security as well as forensic applications.

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil.

PubMed

Chen, Bin; Wang, Yanan; Yan, Zhaoli

2018-01-29

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

Phagocyte-Myocyte Interactions and Consequences during Hypoxic Wound Healing

PubMed Central

Zhang, Shuang; Dehn, Shirley; DeBerge, Matthew; Rhee, KJ; Hudson, Barry; Thorp, Edward

2014-01-01

Myocardial infarction (MI), secondary to atherosclerotic plaque rupture and occlusive thrombi, triggers acute margination of inflammatory neutrophils and monocyte phagocyte subsets to the damaged heart, the latter of which may give rise briefly to differentiated macrophage-like or dendritic-like cells. Within the injured myocardium, a primary function of these phagocytic cells is to remove damaged extracellular matrix, necrotic and apoptotic cardiac cells, as well as immune cells that turn over. Recognition of dying cellular targets by phagocytes triggers intracellular signaling, particularly in macrophages, wherein cytokines and lipid mediators are generated to promote inflammation resolution, fibrotic scarring, angiogenesis, and compensatory organ remodeling. These actions cooperate in an effort to preserve myocardial contractility and prevent heart failure. Immune cell function is modulated by local tissue factors that include secreted protease activity, oxidative stress during clinical reperfusion, and hypoxia. Importantly, experimental evidence suggests that monocyte function and phagocytosis efficiency is compromised in the setting of MI risk factors, including hyperlipidemia and ageing, however underlying mechanisms remain unclear. Herein we review seminal phagocyte and cardiac molecular factors that lead to, and culminate in, the recognition and removal of dying injured myocardium, the effects of hypoxia, and their relationship to cardiac infarct size and heart healing. PMID:24862542

Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations

PubMed Central

Sauter, Disa A.; Eisner, Frank; Ekman, Paul; Scott, Sophie K.

2010-01-01

Emotional signals are crucial for sharing important information, with conspecifics, for example, to warn humans of danger. Humans use a range of different cues to communicate to others how they feel, including facial, vocal, and gestural signals. We examined the recognition of nonverbal emotional vocalizations, such as screams and laughs, across two dramatically different cultural groups. Western participants were compared to individuals from remote, culturally isolated Namibian villages. Vocalizations communicating the so-called “basic emotions” (anger, disgust, fear, joy, sadness, and surprise) were bidirectionally recognized. In contrast, a set of additional emotions was only recognized within, but not across, cultural boundaries. Our findings indicate that a number of primarily negative emotions have vocalizations that can be recognized across cultures, while most positive emotions are communicated with culture-specific signals. PMID:20133790

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.

PubMed

Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant; Jayadeva J

2016-02-01

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Genetic evidence for a tight cooperation of TatB and TatC during productive recognition of twin-arginine (Tat) signal peptides in Escherichia coli.

PubMed

Lausberg, Frank; Fleckenstein, Stefan; Kreutzenbeck, Peter; Fröbel, Julia; Rose, Patrick; Müller, Matthias; Freudl, Roland

2012-01-01

The twin arginine translocation (Tat) pathway transports folded proteins across the cytoplasmic membrane of bacteria. Tat signal peptides contain a consensus motif (S/T-R-R-X-F-L-K) that is thought to play a crucial role in substrate recognition by the Tat translocase. Replacement of the phenylalanine at the +2 consensus position in the signal peptide of a Tat-specific reporter protein (TorA-MalE) by aspartate blocked export of the corresponding TorA(D(+2))-MalE precursor, indicating that this mutation prevents a productive binding of the TorA(D(+2)) signal peptide to the Tat translocase. Mutations were identified in the extreme amino-terminal regions of TatB and TatC that synergistically suppressed the export defect of TorA(D(+2))-MalE when present in pairwise or triple combinations. The observed synergistic suppression activities were even more pronounced in the restoration of membrane translocation of another export-defective precursor, TorA(KQ)-MalE, in which the conserved twin arginine residues had been replaced by lysine-glutamine. Collectively, these findings indicate that the extreme amino-terminal regions of TatB and TatC cooperate tightly during recognition and productive binding of Tat-dependent precursor proteins and, furthermore, that TatB and TatC are both involved in the formation of a specific signal peptide binding site that reaches out as far as the end of the TatB transmembrane segment.

Visual Biopsy by Hydrogen Peroxide-Induced Signal Amplification.

PubMed

Zhao, Wenjie; Yang, Sheng; Yang, Jinfeng; Li, Jishan; Zheng, Jing; Qing, Zhihe; Yang, Ronghua

2016-11-01

Visual biopsy has attracted special interest by surgeons due to its simplicity and practicality; however, the limited sensitivity of the technology makes it difficult to achieve an early diagnosis. To circumvent this problem, herein, we report a visual signal amplification strategy for establishing a marker-recognizable biopsy that enables early cancer diagnosis. In our proposed approach, hydrogen peroxide (H 2 O 2 ) was selected as a potential underlying marker for its compact relationship in cancer progression. For selective recognition of H 2 O 2 in the process of visual biopsy, a benzylbenzeneboronic acid pinacol ester-decorated copolymer, namely, PMPC-Bpe, was synthesized, affording the final formation of the H 2 O 2 -responsive micelles in which amylose was trapped. The presence of H 2 O 2 activates the boronate ester recognition site and induces it releasing abundant indicator amylose, leading to signal amplification. The indicator came across the solution of KI/I 2 added to the sample, and the formative amylose-KI/I 2 complex has a distinct blue color at 574 nm for visual amplification detection. The feasibility of the proposed method is demonstrated by visualizing the H 2 O 2 content of cancer at different stages and three kinds of actual cancerous samples. As far as we know, this is the first paradigm to rationally design a signaling amplification-based molecular recognizable biopsy for visual and sensitive disease identification, which will extend new possibilities for marker-recognition and signal amplification-based biopsy in disease progressing.

Pattern-recognition receptors: signaling pathways and dysregulation in canine chronic enteropathies-brief review.

PubMed

Heilmann, Romy M; Allenspach, Karin

2017-11-01

Pattern-recognition receptors (PRRs) are expressed by innate immune cells and recognize pathogen-associated molecular patterns (PAMPs) as well as endogenous damage-associated molecular pattern (DAMP) molecules. With a large potential for synergism or convergence between their signaling pathways, PRRs orchestrate a complex interplay of cellular mediators and transcription factors, and thus play a central role in homeostasis and host defense. Aberrant activation of PRR signaling, mutations of the receptors and/or their downstream signaling molecules, and/or DAMP/PAMP complex-mediated receptor signaling can potentially lead to chronic auto-inflammatory diseases or development of cancer. PRR signaling pathways appear to also present an interesting new avenue for the modulation of inflammatory responses and to serve as potential novel therapeutic targets. Evidence for a dysregulation of the PRR toll-like receptor (TLR)2, TLR4, TLR5, and TLR9, nucleotide-binding oligomerization domain-containing protein (NOD)2, and the receptor of advanced glycation end products (RAGE) exists in dogs with chronic enteropathies. We describe the TLR, NOD2, and RAGE signaling pathways and evaluate the current veterinary literature-in comparison to human medicine-to determine the role of TLRs, NOD2, and RAGE in canine chronic enteropathies.

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications

PubMed Central

Park, Keunyeol; Song, Minkyu

2018-01-01

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm2 with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency. PMID:29495273

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications.

PubMed

Park, Keunyeol; Song, Minkyu; Kim, Soo Youn

2018-02-24

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm² with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency.

Computer-aided diagnosis of alcoholism-related EEG signals.

PubMed

Acharya, U Rajendra; S, Vidya; Bhat, Shreya; Adeli, Hojjat; Adeli, Amir

2014-12-01

Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans. Copyright © 2014 Elsevier Inc. All rights reserved.

A miniature electronic nose system based on an MWNT-polymer microsensor array and a low-power signal-processing chip.

PubMed

Chiu, Shih-Wen; Wu, Hsiang-Chiu; Chou, Ting-I; Chen, Hsin; Tang, Kea-Tiong

2014-06-01

This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760 μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.

Association of enhanced limbic response to threat with decreased cortical facial recognition memory response in schizophrenia

PubMed Central

Satterthwaite, Theodore D.; Wolf, Daniel H.; Loughead, James; Ruparel, Kosha; Valdez, Jeffrey N.; Siegel, Steven J.; Kohler, Christian G.; Gur, Raquel E.; Gur, Ruben C.

2014-01-01

Objective Recognition memory of faces is impaired in patients with schizophrenia, as is the neural processing of threat-related signals, but how these deficits interact to produce symptoms is unclear. Here we used an affective face recognition paradigm to examine possible interactions between cognitive and affective neural systems in schizophrenia. Methods fMRI (3T) BOLD response was examined in 21 controls and 16 patients during a two-choice recognition task using images of human faces. Each target face had previously been displayed with a threatening or non-threatening affect, but here were displayed with neutral affect. Responses to successful recognition and for the effect of previously threatening vs. non-threatening affect were evaluated, and correlations with total BPRS examined. Functional connectivity analyses examined the relationship between activation in the amygdala and cortical regions involved in recognition memory. Results Patients performed the task more slowly than controls. Controls recruited the expected cortical regions to a greater degree than patients, and patients with more severe symptoms demonstrated proportionally less recruitment. Increased symptoms were also correlated with augmented amygdala and orbitofrontal cortex response to threatening faces. Controls exhibited a negative correlation between activity in the amygdala and cortical regions involved in cognition, while patients showed a weakening of that relationship. Conclusions Increased symptoms were related to an enhanced threat response in limbic regions and a diminished recognition memory response in cortical regions, supporting a link between two brain systems often examined in isolation. This finding suggests that abnormal processing of threat-related signals in the environment may exacerbate cognitive impairment in schizophrenia. PMID:20194482

Dynamic nanoplatforms in biosensor and membrane constitutional systems.

PubMed

Mahon, Eugene; Aastrup, Teodor; Barboiu, Mihail

2012-01-01

Molecular recognition in biological systems occurs mainly at interfacial environments such as membrane surfaces, enzyme active sites, or the interior of the DNA double helix. At the cell membrane surface, carbohydrate-protein recognition principles apply to a range of specific non-covalent interactions including immune response, cell proliferation, adhesion and death, cell-cell interaction and communication. Protein-protein recognition meanwhile accounts for signalling processes and ion channel structure. In this chapter we aim to describe such constitutional dynamic interfaces for biosensing and membrane transport applications. Constitutionally adaptive interfaces may mimic the recognition capabilities intrinsic to natural recognition processes. We present some recent examples of 2D and 3D constructed sensors and membranes of this type and describe their sensing and transport capabilities.

Real-time simultaneous and proportional myoelectric control using intramuscular EMG

PubMed Central

Kuiken, Todd A; Hargrove, Levi J

2014-01-01

Objective Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. Approach We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. Main Results Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts' Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts' Law tasks with high levels of path efficiency. Significance These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control. PMID:25394366

Real-time simultaneous and proportional myoelectric control using intramuscular EMG

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2014-12-01

Objective. Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. Approach. We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. Main results. Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts’ Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts’ Law tasks with high levels of path efficiency. Significance. These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control.

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

PubMed

Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

2015-12-01

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

The Effect of Remote Masking on the Reception of Speech by Young School-Age Children.

PubMed

Youngdahl, Carla L; Healy, Eric W; Yoho, Sarah E; Apoux, Frédéric; Holt, Rachael Frush

2018-02-15

Psychoacoustic data indicate that infants and children are less likely than adults to focus on a spectral region containing an anticipated signal and are more susceptible to remote masking of a signal. These detection tasks suggest that infants and children, unlike adults, do not listen selectively. However, less is known about children's ability to listen selectively during speech recognition. Accordingly, the current study examines remote masking during speech recognition in children and adults. Adults and 7- and 5-year-old children performed sentence recognition in the presence of various spectrally remote maskers. Intelligibility was determined for each remote-masker condition, and performance was compared across age groups. It was found that speech recognition for 5-year-olds was reduced in the presence of spectrally remote noise, whereas the maskers had no effect on the 7-year-olds or adults. Maskers of different bandwidth and remoteness had similar effects. In accord with psychoacoustic data, young children do not appear to focus on a spectral region of interest and ignore other regions during speech recognition. This tendency may help account for their typically poorer speech perception in noise. This study also appears to capture an important developmental stage, during which a substantial refinement in spectral listening occurs.

MDMA (Ecstasy) use is associated with reduced BOLD signal change during semantic recognition in abstinent human polydrug users: a preliminary fMRI study

PubMed Central

Raj, Vidya; Liang, Han-Chun; Woodward, Neil D.; Bauernfeind, Amy L.; Lee, Junghee; Dietrich, Mary; Park, Sohee; Cowan, Ronald L.

2011-01-01

Objectives MDMA users have impaired verbal memory, and voxel-based morphometry has demonstrated decreased gray matter in Brodmann area (BA) 18, 21 and 45. Because these regions play a role in verbal memory, we hypothesized that MDMA users would show altered brain activation in these areas during performance of an fMRI task that probed semantic verbal memory. Methods Polysubstance users enriched for MDMA exposure participated in a semantic memory encoding and recognition fMRI task that activated left BA 9, 18, 21/22 and 45. Primary outcomes were percent BOLD signal change in left BA 9, 18, 21/22 and 45, accuracy and response time. Results During semantic recognition, lifetime MDMA use was associated with decreased activation in left BA 9, 18 and 21/22 but not 45. This was partly influenced by contributions from cannabis and cocaine use. MDMA exposure was not associated with accuracy or response time during the semantic recognition task. Conclusions During semantic recognition, MDMA exposure is associated with reduced regional brain activation in regions mediating verbal memory. These findings partially overlap with prior structural evidence for reduced gray matter in MDMA users and may, in part, explain the consistent verbal memory impairments observed in other studies of MDMA users. PMID:19304866

Dynorphins regulate the strength of social memory.

PubMed

Bilkei-Gorzo, A; Mauer, D; Michel, K; Zimmer, A

2014-02-01

Emotionally arousing events like encounter with an unfamiliar con-species produce strong and vivid memories, whereby the hippocampus and amygdala play a crucial role. It is less understood, however, which neurotransmitter systems regulate the strength of social memories, which have a strong emotional component. It was shown previously that dynorphin signalling is involved in the formation and extinction of fear memories, therefore we asked if it influences social memories as well. Mice with a genetic deletion of the prodynorphin gene Pdyn (Pdyn(-/-)) showed a superior partner recognition ability, whereas their performance in the object recognition test was identical as in wild-type mice. Pharmacological blockade of kappa opioid receptors (KORs) led to an enhanced social memory in wild-type animals, whereas activation of KORs reduced the recognition ability of Pdyn(-/-) mice. Partner recognition test situation induced higher elevation in dynorphin A levels in the central and basolateral amygdala as well as in the hippocampus, and also higher dynorphin B levels in the hippocampus than the object recognition test situation. Our result suggests that dynorphin system activity is increased in emotionally arousing situation and it decreases the formation of social memories. Thus, dynorphin signalling is involved in the formation of social memories by diminishing the emotional component of the experience. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fifty years of progress in speech and speaker recognition

NASA Astrophysics Data System (ADS)

Furui, Sadaoki

2004-10-01

Speech and speaker recognition technology has made very significant progress in the past 50 years. The progress can be summarized by the following changes: (1) from template matching to corpus-base statistical modeling, e.g., HMM and n-grams, (2) from filter bank/spectral resonance to Cepstral features (Cepstrum + DCepstrum + DDCepstrum), (3) from heuristic time-normalization to DTW/DP matching, (4) from gdistanceh-based to likelihood-based methods, (5) from maximum likelihood to discriminative approach, e.g., MCE/GPD and MMI, (6) from isolated word to continuous speech recognition, (7) from small vocabulary to large vocabulary recognition, (8) from context-independent units to context-dependent units for recognition, (9) from clean speech to noisy/telephone speech recognition, (10) from single speaker to speaker-independent/adaptive recognition, (11) from monologue to dialogue/conversation recognition, (12) from read speech to spontaneous speech recognition, (13) from recognition to understanding, (14) from single-modality (audio signal only) to multi-modal (audio/visual) speech recognition, (15) from hardware recognizer to software recognizer, and (16) from no commercial application to many practical commercial applications. Most of these advances have taken place in both the fields of speech recognition and speaker recognition. The majority of technological changes have been directed toward the purpose of increasing robustness of recognition, including many other additional important techniques not noted above.

Time and timing in the acoustic recognition system of crickets

PubMed Central

Hennig, R. Matthias; Heller, Klaus-Gerhard; Clemens, Jan

2014-01-01

The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognition system that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognition systems in insects based on simple sensory computations. PMID:25161622

De Novo Transcriptome Analysis Shows That SAV-3 Infection Upregulates Pattern Recognition Receptors of the Endosomal Toll-Like and RIG-I-Like Receptor Signaling Pathways in Macrophage/Dendritic Like TO-Cells.

PubMed

Xu, Cheng; Evensen, Øystein; Munang'andu, Hetron

2016-04-21

A fundamental step in cellular defense mechanisms is the recognition of "danger signals" made of conserved pathogen associated molecular patterns (PAMPs) expressed by invading pathogens, by host cell germ line coded pattern recognition receptors (PRRs). In this study, we used RNA-seq and the Kyoto encyclopedia of genes and genomes (KEGG) to identify PRRs together with the network pathway of differentially expressed genes (DEGs) that recognize salmonid alphavirus subtype 3 (SAV-3) infection in macrophage/dendritic like TO-cells derived from Atlantic salmon (Salmo salar L) headkidney leukocytes. Our findings show that recognition of SAV-3 in TO-cells was restricted to endosomal Toll-like receptors (TLRs) 3 and 8 together with RIG-I-like receptors (RLRs) and not the nucleotide-binding oligomerization domain-like receptors NOD-like receptor (NLRs) genes. Among the RLRs, upregulated genes included the retinoic acid inducible gene I (RIG-I), melanoma differentiation association 5 (MDA5) and laboratory of genetics and physiology 2 (LGP2). The study points to possible involvement of the tripartite motif containing 25 (TRIM25) and mitochondrial antiviral signaling protein (MAVS) in modulating RIG-I signaling being the first report that links these genes to the RLR pathway in SAV-3 infection in TO-cells. Downstream signaling suggests that both the TLR and RLR pathways use interferon (IFN) regulatory factors (IRFs) 3 and 7 to produce IFN-a2. The validity of RNA-seq data generated in this study was confirmed by quantitative real time qRT-PCR showing that genes up- or downregulated by RNA-seq were also up- or downregulated by RT-PCR. Overall, this study shows that de novo transcriptome assembly identify key receptors of the TLR and RLR sensors engaged in host pathogen interaction at cellular level. We envisage that data presented here can open a road map for future intervention strategies in SAV infection of salmon.

Optical character recognition of camera-captured images based on phase features

NASA Astrophysics Data System (ADS)

Diaz-Escobar, Julia; Kober, Vitaly

2015-09-01

Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.

On the measurement of criterion noise in signal detection theory: the case of recognition memory.

PubMed

Kellen, David; Klauer, Karl Christoph; Singmann, Henrik

2012-07-01

Traditional approaches within the framework of signal detection theory (SDT; Green & Swets, 1966), especially in the field of recognition memory, assume that the positioning of response criteria is not a noisy process. Recent work (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008) has challenged this assumption, arguing not only for the existence of criterion noise but also for its large magnitude and substantive contribution to individuals' performance. A review of these recent approaches for the measurement of criterion noise in SDT identifies several shortcomings and confoundings. A reanalysis of Benjamin et al.'s (2009) data sets as well as the results from a new experimental method indicate that the different forms of criterion noise proposed in the recognition memory literature are of very low magnitudes, and they do not provide a significant improvement over the account already given by traditional SDT without criterion noise. Copyright 2012 APA, all rights reserved.

Recollection is a continuous process: implications for dual-process theories of recognition memory.

PubMed

Mickes, Laura; Wais, Peter E; Wixted, John T

2009-04-01

Dual-process theory, which holds that recognition decisions can be based on recollection or familiarity, has long seemed incompatible with signal detection theory, which holds that recognition decisions are based on a singular, continuous memory-strength variable. Formal dual-process models typically regard familiarity as a continuous process (i.e., familiarity comes in degrees), but they construe recollection as a categorical process (i.e., recollection either occurs or does not occur). A continuous process is characterized by a graded relationship between confidence and accuracy, whereas a categorical process is characterized by a binary relationship such that high confidence is associated with high accuracy but all lower degrees of confidence are associated with chance accuracy. Using a source-memory procedure, we found that the relationship between confidence and source-recollection accuracy was graded. Because recollection, like familiarity, is a continuous process, dual-process theory is more compatible with signal detection theory than previously thought.

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis

PubMed Central

Wrzaczek, Michael; Vainonen, Julia P; Stael, Simon; Tsiatsiani, Liana; Help-Rinta-Rahko, Hanna; Gauthier, Adrien; Kaufholdt, David; Bollhöner, Benjamin; Lamminmäki, Airi; Staes, An; Gevaert, Kris; Tuominen, Hannele; Van Breusegem, Frank; Helariutta, Ykä; Kangasjärvi, Jaakko

2015-01-01

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor. PMID:25398910

GRIM REAPER peptide binds to receptor kinase PRK5 to trigger cell death in Arabidopsis.

PubMed

Wrzaczek, Michael; Vainonen, Julia P; Stael, Simon; Tsiatsiani, Liana; Help-Rinta-Rahko, Hanna; Gauthier, Adrien; Kaufholdt, David; Bollhöner, Benjamin; Lamminmäki, Airi; Staes, An; Gevaert, Kris; Tuominen, Hannele; Van Breusegem, Frank; Helariutta, Ykä; Kangasjärvi, Jaakko

2015-01-02

Recognition of extracellular peptides by plasma membrane-localized receptor proteins is commonly used in signal transduction. In plants, very little is known about how extracellular peptides are processed and activated in order to allow recognition by receptors. Here, we show that induction of cell death in planta by a secreted plant protein GRIM REAPER (GRI) is dependent on the activity of the type II metacaspase METACASPASE-9. GRI is cleaved by METACASPASE-9 in vitro resulting in the release of an 11 amino acid peptide. This peptide bound in vivo to the extracellular domain of the plasma membrane-localized, atypical leucine-rich repeat receptor-like kinase POLLEN-SPECIFIC RECEPTOR-LIKE KINASE 5 (PRK5) and was sufficient to induce oxidative stress/ROS-dependent cell death. This shows a signaling pathway in plants from processing and activation of an extracellular protein to recognition by its receptor. © 2014 The Authors.

Heterospecific eavesdropping in ant-following birds of the Neotropics is a learned behaviour.

PubMed

Pollock, Henry S; Martínez, Ari E; Kelley, J Patrick; Touchton, Janeene M; Tarwater, Corey E

2017-10-25

Animals eavesdrop on other species to obtain information about their environments. Heterospecific eavesdropping can yield tangible fitness benefits by providing valuable information about food resources and predator presence. The ability to eavesdrop may therefore be under strong selection, although extensive research on alarm-calling in avian mixed-species flocks has found only limited evidence that close association with another species could select for innate signal recognition. Nevertheless, very little is known about the evolution of eavesdropping behaviour and the mechanism of heterospecific signal recognition, particularly in other ecological contexts, such as foraging. To understand whether heterospecific eavesdropping was an innate or learned behaviour in a foraging context, we studied heterospecific signal recognition in ant-following birds of the Neotropics, which eavesdrop on vocalizations of obligate ant-following species to locate and recruit to swarms of the army ant Eciton burchellii , a profitable food resource. We used a playback experiment to compare recruitment of ant-following birds to vocalizations of two obligate species at a mainland site (where both species are present) and a nearby island site (where one species remains whereas the other went extinct approx. 40 years ago). We found that ant-following birds recruited strongly to playbacks of the obligate species present at both island and mainland sites, but the island birds did not recruit to playbacks of the absent obligate species. Our results strongly suggest that (i) ant-following birds learn to recognize heterospecific vocalizations from ecological experience and (ii) island birds no longer recognize the locally extinct obligate species after eight generations of absence from the island. Although learning appears to be the mechanism of heterospecific signal recognition in ant-following birds, more experimental tests are needed to fully understand the evolution of eavesdropping behaviour. © 2017 The Author(s).

Target recognitions in multiple-camera closed-circuit television using color constancy

NASA Astrophysics Data System (ADS)

Soori, Umair; Yuen, Peter; Han, Ji Wen; Ibrahim, Izzati; Chen, Wentao; Hong, Kan; Merfort, Christian; James, David; Richardson, Mark

2013-04-01

People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people's dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.

OPTICAL INFORMATION PROCESSING: Synthesis of an object recognition system based on the profile of the envelope of a laser pulse in pulsed lidars

NASA Astrophysics Data System (ADS)

Buryi, E. V.

1998-05-01

The main problems in the synthesis of an object recognition system, based on the principles of operation of neuron networks, are considered. Advantages are demonstrated of a hierarchical structure of the recognition algorithm. The use of reading of the amplitude spectrum of signals as information tags is justified and a method is developed for determination of the dimensionality of the tag space. Methods are suggested for ensuring the stability of object recognition in the optical range. It is concluded that it should be possible to recognise perspectives of complex objects.

Does humor in radio advertising affect recognition of novel product brand names?

PubMed

Berg, E M; Lippman, L G

2001-04-01

The authors proposed that item selection during shopping is based on brand name recognition rather than recall. College students rated advertisements and news stories of a simulated radio program for level of amusement (orienting activity) before participating in a surprise recognition test. Humor level of the advertisements was varied systematically, and content was controlled. According to signal detection analysis, humor did not affect the strength of recognition memory for brand names (nonsense units). However, brand names and product types were significantly more likely to be associated when appearing in humorous advertisements than in nonhumorous advertisements. The results are compared with prior findings concerning humor and recall.

Emotional System for Military Target Identification

DTIC Science & Technology

2009-10-01

algorithm [23], and used it to solve a facial recognition problem. In other works [24,25], we explored the potential of using emotional neural...other application areas, such as security ( facial recognition ) and medical (blood cell identification), can be also efficiently used in military...Application of an emotional neural network to facial recognition . Neural Computing and Applications, 18(4), 309-320. [25] Khashman, A. (2009). Blood cell

[Emotional facial expression recognition impairment in Parkinson disease].

PubMed

Lachenal-Chevallet, Karine; Bediou, Benoit; Bouvard, Martine; Thobois, Stéphane; Broussolle, Emmanuel; Vighetto, Alain; Krolak-Salmon, Pierre

2006-03-01

some behavioral disturbances observed in Parkinson's disease (PD) could be related to impaired recognition of various social messages particularly emotional facial expressions. facial expression recognition was assessed using morphed faces (five emotions: happiness, fear, anger, disgust, neutral), and compared to gender recognition and general cognitive assessment in 12 patients with Parkinson's disease and 14 controls subjects. facial expression recognition was impaired among patients, whereas gender recognitions, visuo-perceptive capacities and total efficiency were preserved. Post hoc analyses disclosed a deficit for fear and disgust recognition compared to control subjects. the impairment of emotional facial expression recognition in PD appears independent of other cognitive deficits. This impairment may be related to the dopaminergic depletion in basal ganglia and limbic brain regions. They could take a part in psycho-behavioral disorders and particularly in communication disorders observed in Parkinson's disease patients.

Memory monitoring by animals and humans

NASA Technical Reports Server (NTRS)

Smith, J. D.; Shields, W. E.; Allendoerfer, K. R.; Washburn, D. A.; Rumbaugh, D. M. (Principal Investigator)

1998-01-01

The authors asked whether animals and humans would use similarly an uncertain response to escape indeterminate memories. Monkeys and humans performed serial probe recognition tasks that produced differential memory difficulty across serial positions (e.g., primacy and recency effects). Participants were given an escape option that let them avoid any trials they wished and receive a hint to the trial's answer. Across species, across tasks, and even across conspecifics with sharper or duller memories, monkeys and humans used the escape option selectively when more indeterminate memory traces were probed. Their pattern of escaping always mirrored the pattern of their primary memory performance across serial positions. Signal-detection analyses confirm the similarity of the animals' and humans' performances. Optimality analyses assess their efficiency. Several aspects of monkeys' performance suggest the cognitive sophistication of their decisions to escape.

(abstract) A High Throughput 3-D Inner Product Processor

NASA Technical Reports Server (NTRS)

Daud, Tuan

1996-01-01

A particularily challenging image processing application is the real time scene acquisition and object discrimination. It requires spatio-temporal recognition of point and resolved objects at high speeds with parallel processing algorithms. Neural network paradigms provide fine grain parallism and, when implemented in hardware, offer orders of magnitude speed up. However, neural networks implemented on a VLSI chip are planer architectures capable of efficient processing of linear vector signals rather than 2-D images. Therefore, for processing of images, a 3-D stack of neural-net ICs receiving planar inputs and consuming minimal power are required. Details of the circuits with chip architectures will be described with need to develop ultralow-power electronics. Further, use of the architecture in a system for high-speed processing will be illustrated.

Disk space and load time requirements for eye movement biometric databases

NASA Astrophysics Data System (ADS)

Kasprowski, Pawel; Harezlak, Katarzyna

2016-06-01

Biometric identification is a very popular area of interest nowadays. Problems with the so-called physiological methods like fingerprints or iris recognition resulted in increased attention paid to methods measuring behavioral patterns. Eye movement based biometric (EMB) identification is one of the interesting behavioral methods and due to the intensive development of eye tracking devices it has become possible to define new methods for the eye movement signal processing. Such method should be supported by an efficient storage used to collect eye movement data and provide it for further analysis. The aim of the research was to check various setups enabling such a storage choice. There were various aspects taken into consideration, like disk space usage, time required for loading and saving whole data set or its chosen parts.

Finding Semirigid Domains in Biomolecules by Clustering Pair-Distance Variations

PubMed Central

Schreiner, Wolfgang

2014-01-01

Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction. PMID:24959586

Cognitive Radio Low-Energy Signal Analysis Sensor Integrated Circuits (CLASIC): A Broadband Mixed-Signal Iterative Down Conversion Spectrum Analyzer for Signal Recognition Applications

DTIC Science & Technology

2015-12-01

AFRL-RY-WP-TR-2015-0144 COGNITIVE RADIO LOW-ENERGY SIGNAL ANALYSIS SENSOR INTEGRATED CIRCUITS (CLASIC) A Broadband Mixed-Signal Iterative Down...See additional restrictions described on inside pages STINFO COPY AIR FORCE RESEARCH LABORATORY SENSORS DIRECTORATE WRIGHT-PATTERSON AIR FORCE...Signature// TODD KASTLE, Chief Spectrum Warfare Division Sensors Directorate This report is published in the interest of scientific and technical

Very low-frequency signals support perceptual organization of implant-simulated speech for adults and children

PubMed Central

Nittrouer, Susan; Tarr, Eric; Bolster, Virginia; Caldwell-Tarr, Amanda; Moberly, Aaron C.; Lowenstein, Joanna H.

2014-01-01

Objective Using signals processed to simulate speech received through cochlear implants and low-frequency extended hearing aids, this study examined the proposal that low-frequency signals facilitate the perceptual organization of broader, spectrally degraded signals. Design In two experiments, words and sentences were presented in diotic and dichotic configurations as four-channel noise-vocoded signals (VOC-only), and as those signals combined with the acoustic signal below 250 Hz (LOW-plus). Dependent measures were percent correct recognition scores, and the difference between scores for the two processing conditions given as proportions of recognition scores for VOC-only. The influence of linguistic context was also examined. Study Sample Participants had normal hearing. In all, 40 adults, 40 7-year-olds, and 20 5-year-olds participated. Results Participants of all ages showed benefits of adding the low-frequency signal. The effect was greater for sentences than words, but no effect of configuration was found. The influence of linguistic context was similar across age groups, and did not contribute to the low-frequency effect. Listeners who scored more poorly with VOC-only stimuli showed greater low-frequency effects. Conclusion The benefit of adding a very low-frequency signal to a broader, spectrally degraded signal seems to derive from its facilitative influence on perceptual organization of the sensory input. PMID:24456179

Applications of sub-audible speech recognition based upon electromyographic signals

NASA Technical Reports Server (NTRS)

Jorgensen, C. Charles (Inventor); Betts, Bradley J. (Inventor)

2009-01-01

Method and system for generating electromyographic or sub-audible signals (''SAWPs'') and for transmitting and recognizing the SAWPs that represent the original words and/or phrases. The SAWPs may be generated in an environment that interferes excessively with normal speech or that requires stealth communications, and may be transmitted using encoded, enciphered or otherwise transformed signals that are less subject to signal distortion or degradation in the ambient environment.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-09-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-08-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition.

PubMed

Zhang, Yifan; Gao, Xunzhang; Peng, Xuan; Ye, Jiaqi; Li, Xiang

2018-05-16

The High Resolution Range Profile (HRRP) recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). However, traditional HRRP recognition methods failed to model high dimensional sequential data efficiently and have a poor anti-noise ability. To deal with these problems, a novel stochastic neural network model named Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed in this paper. RTRBM is utilized to extract discriminative features and the attention mechanism is adopted to select major features. RTRBM is efficient to model high dimensional HRRP sequences because it can extract the information of temporal and spatial correlation between adjacent HRRPs. The attention mechanism is used in sequential data recognition tasks including machine translation and relation classification, which makes the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and the attention mechanism makes our model effective for extracting more internal related features and choose the important parts of the extracted features. Additionally, the model performs well with the noise corrupted HRRP data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, which indicates that ARTRBM extracts, selects, and utilizes the correlation information between adjacent HRRPs effectively and is suitable for high dimensional data or noise corrupted data.

Proactive Interference Slows Recognition by Eliminating Fast Assessments of Familiarity

ERIC Educational Resources Information Center

Oztekin, Ilke; McElree, Brian

2007-01-01

The response-signal speed-accuracy tradeoff (SAT) procedure was used to investigate how proactive interference (PI) affects retrieval from working memory. Participants were presented with 6-item study lists, followed immediately by a recognition probe. A variant of a release from PI design was used: All items in a list were from the same semantic…

Separating Mnemonic Process from Participant and Item Effects in the Assessment of ROC Asymmetries

ERIC Educational Resources Information Center

Pratte, Michael S.; Rouder, Jeffrey N.; Morey, Richard D.

2010-01-01

One of the most influential findings in the study of recognition memory is that receiver operating characteristic (ROC) curves are asymmetric about the negative diagonal. This result has led to the rejection of the equal-variance signal detection model of recognition memory and has provided motivation for more complex models, such as the…

Western pine beetle populations in Arizona and California differ in the composition of their aggregation pheromones

Treesearch

Deepa S. Pureswaran; Richard W. Hofstetter; Brian T. Sullivan; Amanda M. Grady; Cavell Brownie

2016-01-01

When related species coexist, selection pressure should favor evolution of species recognition mechanisms to prevent interspecific pairing and wasteful reproductive encounters. We investigated the potential role of pheromone and acoustic signals in species recognition between two species of tree-killing bark beetles, the southern pine beetle, Dendroctonus frontalis...

[The role of temporal fine structure in tone recognition and music perception].

PubMed

Zhou, Q; Gu, X; Liu, B

2017-11-07

The sound signal can be decomposed into temporal envelope and temporal fine structure information. The temporal envelope information is crucial for speech perception in quiet environment, and the temporal fine structure information plays an important role in speech perception in noise, Mandarin tone recognition and music perception, especially the pitch and melody perception.

Some Memories Are Odder than Others: Judgments of Episodic Oddity Violate Known Decision Rules

ERIC Educational Resources Information Center

O'Connor, Akira R.; Guhl, Emily N.; Cox, Justin C.; Dobbins, Ian G.

2011-01-01

Current decision models of recognition memory are based almost entirely on one paradigm, single item old/new judgments accompanied by confidence ratings. This task results in receiver operating characteristics (ROCs) that are well fit by both signal-detection and dual-process models. Here we examine an entirely new recognition task, the judgment…

Overview of radar intra-pulse modulation recognition

NASA Astrophysics Data System (ADS)

Zang, Hanlin; Li, Yanling

2018-05-01

This paper introduces the current radar intra-pulse modulation method, describes the status quo and development direction of the intentional modulation and unintentional modulation in the pulse, and summarizes the existing problems and prospects for the future. Looking forward to the future, and providing a reference direction for the research on radar signal recognition in the next step.

Gender recognition from unconstrained and articulated human body.

PubMed

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

Gender Recognition from Unconstrained and Articulated Human Body

PubMed Central

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

Direct Ubiquitin Independent Recognition and Degradation of a Folded Protein by the Eukaryotic Proteasomes-Origin of Intrinsic Degradation Signals

PubMed Central

Singh Gautam, Amit Kumar; Balakrishnan, Satish; Venkatraman, Prasanna

2012-01-01

Eukaryotic 26S proteasomes are structurally organized to recognize, unfold and degrade globular proteins. However, all existing model substrates of the 26S proteasome in addition to ubiquitin or adaptor proteins require unstructured regions in the form of fusion tags for efficient degradation. We report for the first time that purified 26S proteasome can directly recognize and degrade apomyoglobin, a globular protein, in the absence of ubiquitin, extrinsic degradation tags or adaptor proteins. Despite a high affinity interaction, absence of a ligand and presence of only helices/loops that follow the degradation signal, apomyoglobin is degraded slowly by the proteasome. A short floppy F-helix exposed upon ligand removal and in conformational equilibrium with a disordered structure is mandatory for recognition and initiation of degradation. Holomyoglobin, in which the helix is buried, is neither recognized nor degraded. Exposure of the floppy F-helix seems to sensitize the proteasome and primes the substrate for degradation. Using peptide panning and competition experiments we speculate that initial encounters through the floppy helix and additional strong interactions with N-terminal helices anchors apomyoglobin to the proteasome. Stabilizing helical structure in the floppy F-helix slows down degradation. Destabilization of adjacent helices accelerates degradation. Unfolding seems to follow the mechanism of helix unraveling rather than global unfolding. Our findings while confirming the requirement for unstructured regions in degradation offers the following new insights: a) origin and identification of an intrinsic degradation signal in the substrate, b) identification of sequences in the native substrate that are likely to be responsible for direct interactions with the proteasome, and c) identification of critical rate limiting steps like exposure of the intrinsic degron and destabilization of an unfolding intermediate that are presumably catalyzed by the ATPases. Apomyoglobin emerges as a new model substrate to further explore the role of ATPases and protein structure in proteasomal degradation PMID:22506054

Some Memories are Odder than Others: Judgments of Episodic Oddity Violate Known Decision Rules

PubMed Central

O’Connor, Akira R.; Guhl, Emily N.; Cox, Justin C.; Dobbins, Ian G.

2011-01-01

Current decision models of recognition memory are based almost entirely on one paradigm, single item old/new judgments accompanied by confidence ratings. This task results in receiver operating characteristics (ROCs) that are well fit by both signal-detection and dual-process models. Here we examine an entirely new recognition task, the judgment of episodic oddity, whereby participants select the mnemonically odd members of triplets (e.g., a new item hidden among two studied items). Using the only two known signal-detection rules of oddity judgment derived from the sensory perception literature, the unequal variance signal-detection model predicted that an old item among two new items would be easier to discover than a new item among two old items. In contrast, four separate empirical studies demonstrated the reverse pattern: triplets with two old items were the easiest to resolve. This finding was anticipated by the dual-process approach as the presence of two old items affords the greatest opportunity for recollection. Furthermore, a bootstrap-fed Monte Carlo procedure using two independent datasets demonstrated that the dual-process parameters typically observed during single item recognition correctly predict the current oddity findings, whereas unequal variance signal-detection parameters do not. Episodic oddity judgments represent a case where dual- and single-process predictions qualitatively diverge and the findings demonstrate that novelty is “odder” than familiarity. PMID:22833695